Unbeatable Who Seeks Push Yourself Forward
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Contents
Welcome
Interview
Flowing Material Balance in Oil and Gas Reservoirs
Tu t o r i a l
Educational
AAPG Suez Achievements
Dr. Mohamed Farouk Ms. Denise Cox
-Well Testing Challenges in Unconventional and Tight-Shaled Reservoirs -The Red Sea Exploration and prospectivity
Shout
Role of Uncertainty Analysis & Machine Learning in History Matching of Reservoir Simulation Models
Review
Evaluation
Research
Ensemble-Based Reservoir Modeling
Relative rock permeability: An alternative to estimate reservoir properties from seismic data
Application of new IPR model with a comparison to PGOR test and PLT data to evaluate wells productivity
N e w Te c h n o l o g y
Case Study
Pulses
Calculating Brittleness Index Using Triple Combo Logs
Unbeatable Who Seeks The pursuit is the power that forces a person to break their limits and to achieve their goals. I am totally confident that the successful one is the person who seeks continuous development, but we should know that the character cannot be developed in ease and quiet. Only through experience of trial and suffering can the soul be strengthened, ambition inspired, and success achieved. There will be obstacles. There will be doubters. There will be mistakes. But with hard work, there are no limits, so ambitions should be accompanied by hard work because hard work is the most important key to success. Achievements without hard work are impossible. The idle people can never gain anything if they sit and wait for a better opportunity to come their way. The person who is seeking and working hard can achieve success and happiness in life. The constant vigilance and preparedness to work is the price we must pay for success in life. Work is a privilege and a pleasure; the idleness is a luxury that no one can afford. Man is born to work and prosper in life. He, like steel, shines in use and rusts in rest. “We all have the same 24 hours each day� -Zig Ziglar. I believe the difference is how seekers use them. Those people become unbeatable because they consider every second as a big chance to move toward to success. They always tell themselves no matter how life is hard, no matter what the circumstances are. They keep fighting and moving forward until they reach success. From this point of view, I decided to break away from my comfort zone by joining AAPG SU SC. It has been a journey of discovering the right meaning of seeking to develop myself. It has been a journey of learning how to be skilled and of unlimited ambition. Here, I learned that success usually comes to those who keep themselves too busy to seek it. PetroPulse is considered one of the strongest connections between the oil and gas industry and the students who are still studying. It brings you high-profile industry experts’ opinions, experiences, and advice through interviews, meetings, and articles. It also spreads the awareness among students about the latest innovations and new technologies introduced and developed recently in the oil and gas industry. Personally, it has been a great honor and a privilege to provide this wonderful effort to all hoping to achieve the maximum benefit possible and to be a significant addition to our successful projects through season 11. Finally, I would like to express my warmest and sincerest gratitude to the most powerful team of Editors and Designers who have worked really their fingers to the bone to present this issue of PetroPulse. We entered, we altered, and we innovated!
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Push Yourself Forward You are always a step away from gaining new experience, achieving great things or being great yourself. Taking that step definitely requires courage to explore what is beyond it and, of course, standing still requires nothing but it surely takes a lot, away. I always choose not to stand still and to see beyond every step, and I can probably say that the most significant step I have ever taken so far is joining AAPG Suez. “If everyone is moving forward together, then success takes care of itself.” -Henry Ford. In every different experience you will go through you are never on your own, there are always those who will help and lend you a hand to take steps on your road to success but it will not do unless you push yourself forward. Here, I have come to witness a thing everybody talks about that being in AAPG Suez is always challenging. Of course it is! and that is the perfect place to invest your potentials in. The challenge is there are so many who are very special in what they do, but being AAPGians always makes them glad to share and be keen on delivering their experience to all. Being that special has led to a natural fully-deserved achievement to unprecedentedly be the Outstanding International Student Chapter twice in a row so far. Being here, with every one of those one-of-a-kind people, has urged me to work very special and be very special and that has moved me forward until today that I am the President of the place that changed my scope and so, my whole life. For the 11th season, we decided to achieve a vision of being the Technical Portal, but technicalities are usually consumed very traditionally and innovation has always been our specialty and a target to everyone who enters, so there we had “Enter, Alter, Innovate” as our perfect slogan for achieving such a vision. Towards achieving our goals, committed members, diligent board and dedicated high board spared no efforts to think of the best, plan for the best and executing everything in the best way possible. It has led us to deliver our activities in the most professional way and even innovate new ones presenting remarkable value. It has also led us towards fulfilling unprecedented achievements like being the Africa Region representative in the international competition organized by AAPG, the Imperial Barrel Award (IBA). Whether you are a student aiming at moving from one stage to another, a graduate striving for a proper opportunity in the job market, or an employee seeking to keep up and keep promoting, you should know this very well. Pushing yourself forward is the thing that will make you not only move from one stage to another, but also be always in the top of all others. It is what will grant you the most prestigious opportunity in the job market that fits your passion, not just a traditional one. It will make you a pioneer in your own place and your own field, not just keeping up seeking promotions. It is what will make you alive, not just living. So, take every step ahead looking forward. Great experience is always there for you to explore and greater achievements to fulfill.
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Dr. Farouk, let’s start from the beginning. In 1991, I graduated from Cairo University. After I majored in electrical engineering, I got master degree in the diagnosis of defects in industrial and chemical systems using artificial intelligence. Later in 1997, I got my PhD from America in model-based approach for defects diagnosis. That is when I joined Invensys where we built an engineering center for software. It was great to see this work get internationally promoted in India and Mexico. Then I became Global Senior Vice President for Industrial Operational Management, serving reputable clients like Chevron, Aramco, X-Mobil, and others. In 2011, I decided to come back to Egypt and that is when I joined ADES as CEO. Tell us more about your role at ADES as CEO. When I first started with ADES, our goal was to promote our operations. Studying the market, we found a great need for drilling rigs in the Gulf of Suez. Back then, this market was too economically risky. We exploited this chance and managed to provide the rigs in competitive prices and according to standards. We had deals with big companies such as BP and Eni. As CEO, my duty is to promote and optimize the organization and operations of ADES. We give much care for all technological advances that enhance our
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productivity and, of course, we spare no effort when it comes to quality training and sustainable development. Such experience must come with many challenges. What would you consider the most overwhelming? Technically, attracting new talents for our operations was difficult. Starting business with national and international organizations and trying to prove how good the business we can provide is to them was also a great challenge that we managed to overcome and now we have the unquestioned trust of all of our clients. What is the role of ADES in the oil and gas industry? In Egypt, our main focus is offshore operations in the Gulf of Suez. We have 7 drilling rigs and one barge. We have a mobile offshore production unit that enhances production. This our presence in Egypt, but we believe we are more of a regional player. We have a strong presence in Saudi Arabia with 17 rigs, in Kuwait with 8, and in Algeria with 5. We believe we are filling the rapidly increaing gap due to operational risks and regional conflict. This does not appeal to foreign investors because the risks are too high, which creates more chances for us as a local regional player to build stable businesses. Although ADES has been internationally booming since 2015, our main focus is still to serve our original
market in the Gulf of Suez. We are not willing to take surprising leaps internationally, but we can expand from Algeria to Tunisia and from Saudi Arabia to the United Arab Emirates. How do you see the future of ECDC under ADES? I see ECDC as a great investment. Our role is to promote their work by providing the necessary advice and support to help satisfy the market needs. What new targets is ADES looking forward to achieving after 2020? One of the most exciting steps we are taking is ADES Academy. It is a new project empowered by the vision to minimize the manpower losses in the market by working on young engineers to develop their skills and enable them to do more and promote faster. Given the increasing attention for renewable clean energy, how is ADES working to sustain its activity? I always say that there is a huge gap between what you want and what you actually get. The ambition and effort for clean energy are not only justified, but also important. However, in order to achieve renewable zero-emission energy, you will have to pay a very steep price. Despite this attention, 90% of the world still runs on hydrocarbons. So, oil and gas and the associated services are still widely needed. Our role is to empower safety and operational excellence using technology to minimize the negative effects. That is what we are working on right now, we give much care to digital systems that maximize our benefits and add more value. How far do you think Artificial Intelligence and IT are replacing humans in the industry? I believe this is happening right now, either for economical or environmental reasons. In Alaska, for example, the use of advanced technology and artificial intelligence is mainly driven by safety aspects due to the impossible weather conditions. In terms of economics, a machine is more efficient on the long term than humans as it costs less for the same outcome, especially in places like America where standards of life are high. For our region, I would expect the need for advanced technology to emerge in 15-20 years, because we do not have these drivers. In the same time, our market is not ready to operate, support, maintain, or supervise that kind of complex technology.
I am open to the possibility of robotics taking over 100% of the operations even though there is still nothing to support that this is happening soon. What criteria does ADES test when it comes to the hiring of fresh graduates? We look for petroleum, mechanical, and electrical engineering students with at least good cumulative academic grade. But most importantly, we look for leaders who have life-long thirst for development. This kind of leaders will be trusted to blend in the workplace, manage people better, and promote our work at ADES. We give great care to our filtration process based on accurate tests structured by the AUC and other experts. We always make sure to take in 20-30 fresh graduates into our programs, hoping that in a few years they will be qualified enough to start their careers with us. What advice would you give to students and young professionals? First of all, you have to have a plan. Build a 5-10 year plan for yourself and your career development, and try to develop the commitment and discipline to achieve that plan. While pursuing your plan, always remember that you need to find a place where you feel satisfied and motivated. Look for a healthy and nurturing work environment just as hard as you look for fair salaries, and have the courage to make the tough decisions that empower your values. Find the best mix of the four essential factors: IQ, discipline, emotional (social) intelligence, and technical competence. Stay modest and nice to everyone but believe in your ability to achieve whatever you seek and keep working on that. Finally, leave a rich legacy. Build your career and connections over the values you want to stay in the room after you leave. Your legacy can be technical, managerial, or even ethical.
What do you think of AAPG Suez? I believe it is a very good idea and well-directed effort to have an entity that deeply links the industry to the education and university life.
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Let’s start with an introduction about yourself and your journey as a geologist. I found my passion towards geoscience over an AAPG visiting lecture by geologist Susan Landon in Binghamton, New York. That is why I started my journey with the US Geological Survey (USGS) working on the Uranium resource evaluation project in New Mexico. I graduated from the University of Colorado, took evening seminars at the Colorado School of Mines, and thankfully enrolled in classes for Arabic language. Studying Arabic empowered my vital decision to study the geology of Morocco’s High Atlas Mountains under a PhD student doing research in Morocco; an experience that put me on track to become a consultant and then a full-time geologist for Marathon Oil Company’s Denver research center. I then transferred to Texas where I could apply my reservoir research; met my husband, Kurt Cox; and joined the company that he launched, Storm Energy. What would you consider your most significant lessons, turning points, or personal motivators? I believe this is a question with a lifetime of answers. I will offer a summary with this: “Respect the past, act in the present, and lead into the future”. Learn from experienced individuals, fully engage with technical and professional groups to act in the present, then step up to leadership to implement your best ideas. That is how AAPG has been an important part of my career. I learned from individuals who were more experienced than me.
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I volunteered to serve on and chair committees to develop leadership skills then when asked to stand for President of AAPG, I said, “yes”. What do you consider the most difficult challenges through your career? Your life and career will present challenges to you on multiple fronts: technical, managerial, and personal. The challenge will be to balance your equation for personal fulfillment and happiness. I am someone who is driven by intellectual curiosity, so my career was initially dominated by research and applied technology. As I gained experience, I saw the need for more diversity in the petroleum industry and took on leadership roles in industry and professional societies. As President of AAPG, I spent almost all of my time to speak globally to universities and professional societies on sustainable energy development; a topic of personal importance to me. My hometown of Panama City, Florida was destroyed by a category 5 hurricane in 2018, so now my equation is balanced by more time for my family and help the community to rebuild. What is your best accomplishment in geology? My biggest technical accomplishment in geoscience is a regional understanding of the Pennsylvanian of the Eastern Shelf of the Permian Basin. Someday I may take the time to publish that work. My most important personal accomplishment is being a role model for the next generation of geoscientists to bring their technical skills and
diverse perspectives to the petroleum industry for sustainable energy development. In your opinion, what are the major changes in the oil and gas industry after 2020, and how should leaders be preparing to adapt to such changes? Financial markets and social license to operate have caused the industry to address sustainability and more fully address diversity, environmental issues, and social concerns. 2020 will mark the year the petroleum industry begins to implement sustainability as the way we do business. How does the Coronavirus outbreak affect the industry globally and what should leaders do to reverse negative effects? The Coronavirus helped demonstrate how effectively the petroleum industry (and most businesses) can work remotely. The decreased carbon footprint from a physical office to a virtual office may be one positive outcome from the virus. The decreased demand on petroleum, over supply, and resultant low oil prices are a negative COVID-19 effect. Companies have had to delay projects, shut-in wells, and lay off employees. This is the time to redirect employees to explore for natural gas, evaluate acquisitions for redevelopment with new technology, and evaluate mature reservoirs for carbon capture use and storage (CCUS).
How do you see the oil and gas industry after 2020 given the world’s ever-increasing demand for environmentally cleaner alternatives? Even under a 2-degree scenario, oil and gas are still predicted to supply nearly 50% of global energy demand. Exploration and development projects will be needed to replace the natural decline of existing production to meet the global demand. My hope is that all projects will be done within a framework of sustainable energy development. Exploration should include more natural gas, development projects will use technology to maximize recovery of reserves, and all companies will invest in carbon capture projects (CCUS/CCS). Technology is evolving in the oil and gas industry, how do you see its effect on geologists and how far do you think technology is taking over? I interpret this question as addressing geoscience and artificial intelligence. First, I believe it is not “artificial” but “augmented” intelligence. A geoscientist must decide what is important, define the parameters, and build the database to train the system. The geoscientist must write the code or communicate the parameters to the coder. After the program runs, the geoscientist is the one who can interpret the results, decide whether they make sense, and make decisions to act or to improve the results accuracy. Humans are critical to all these phases of AI.
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TUTORIAL
FLOWING MATERIAL BALANCE IN OIL AND GAS RESERVOIRS
Mohamed Amr Aly Reservoir Engineer, ENI, SPA, Italy
Introduction The conventional ways of estimating the hydrocarbon initially in place are Material Balance Equations, Volumetric Methods, and Numerical Simulation Models. These equations are based on dynamic data analysis, so they require historical production data and periodic measurements or estimation of reservoir pressure. However, in most of the cases, for business-related reasons, a filed development plan has to be defined within the first or the second year of the production life of a reservoir when the amount of available information is limited. Flowing Material Balance (FMB) was introduced as a recent approach of production Analysis in which production data is used through an iterative approach to estimate the initial hydrocarbon in place. For Dead Oil: We use an approach introduced by (Mattar & Anderson, 2005), which is valid for constant and variable flow rate conditions. The approach aims to estimate the oil in place using the fluid flow equation in pseudo-steady-state conditions and combining it with the depletion equation for dead oil reservoirs.
The total compressibility is used to give more reliable results to the ones obtained from the RTA. The usage of oil compressibility in the multi-well model overestimates the oil in place, which is opposite where: bpss is the reciprocal of productivity index. A Cartesian plot of (Pi−Pwf )/qo vs. tc will yield a straight to what happened with the single well model. line with an intercept of bpss. Then Average Pressure Comparing results, the flowing material balance approach gives the best accuracy. could be obtained by:
A Cartesian plot of (Pwf − Pi) vs. Np/Co will yield a straight line with a slope equal to 1/N. Because of the neglecting of the skin effect, the permeability estimated by the flowing material balance overestimated.
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For Dry Gas: The development of the FMB for gas reservoir requires the production flowing data and the representing PVT data for each production data, to account for the change of the gas properties change with the continuous production. So (Mattar & Anderson, 2005) introduced another function called the material balance pseudo time to account for gas properties to change with time. This method is based on driving the FMB equation from the fluid flow equation and combining it with the conventional material balance equation.
TUTORIAL The first approach introduced by (McNeil, 1995), needs to have valid PVT data representing the production points because it is mainly Pwf/Z plotting vs. Gp. Therefore, the approach we used to assume an initial G and by the correlation built using PVT database on Ecrin-Topaze might give a high error.
Setting Dietz factor for a single well symmetrically located in a rectangular drainage area as 30.9 Using the material balance equation for gas introduced (Mattar & Anderson, 2005).
Dead Oil Multi-well Although all the approaches underestimate the OOIP in the multi-well model, the underestimation did not exceed 3% of the OOIP (except for FMB using Oil Combining the last 2 equations, we get: compressibility that overestimated the OOIP) which confirms the validation of the approaches used in estimating the OOIP. Flowing Material Balance for Dead Oil overestimated the permeability obtained by the analysis of the production data assuming no skin around the wellbore. This confirms that obtaining PVT representing the production data would generally improve the quality of estimation for oil PVT were considered constant over the production profile and a small change in the data used would highly affect the results on both Now, from bpss, we can have an estimation of the gas OOIP and permeability. effective permeability k if we know h. Dry Gas Multi-Well By plotting (m(Pi) − m(Pwf ))/qg vs. tca, we get ba, pss as For Flowing Material Balance approaches, the contrary intercept. Then we get Pavg to build the conventional of what was experienced in the single well model P/z plot. happened. The Agarwal – Gardner approach showed tca, here is developed from the explicit Pseudo its validation for estimating OGIP over the FMB material balance function, a comparison between approach using material balance time. Although the the two methods is developed to see the effect on normalized pseudo cumulative function showed its calculating the permeability and the gas in place. equivalence with the material balance time function The FMB approaches are implemented for each well, or it is even better as it is not sensitive to the time individually where the results obtained represent the step and the gas properties change is considered Gas originally in place in the drainage area covered as a function of the cumulative production, not as a by each well. function of time, the application of the new material The same procedure developed for the single well balance approach using the normalized pseudo is performed for the multi-well model each well cumulative function still overestimated the OGIP and individually, and the results are obtained in the again this is a result of the PVT data. following figures.
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Well-Testing Challenges in Unconventional and Tight-Gas-Formation Reservoirs
Unconventional reservoirs are hydrocarbon resources with low permeability that cannot be economically produced without stimulation. The unconventi o n a l g a s reservoirs considered in this study include shale-gas formations, tight gas formations, and coalbed methane. Various testing methods have emerged over the years, suiting different formations.
Mehdi Azari
Senior Reservoir Eng. Advisor at Halliburton
Farrukh Hamza
Reservoir Engineering Team Lead at Halliburton
Sandeep Ramakrishna Formation Evaluation specialist at Halliburton
Six case studies are provided in this article to showcase different issues related to well testing in tight-gas-formation wells. For the first two cases, the duration of the PBU was not long enough to overcome the high WBS which was from a surface shut in. Thus, no radial flow could be identified. A downhole shut-in with smaller tubing sizes will reduce the wellbore storage; therefore, it will get to infinite-acting radial flow (IARF) earlier for the PBU analysis. Case 1: Surface-Shut-In DST After completion, a tight-gas-formation well was lifted with nitrogen at 300–500 scf/min from 5,000 ft to the bottom of the well at 14,000 ft using coiled tubing (CT). The well was then shut in at the surface with downhole gauges for a 24-hour PBU. The nitrogen lifting helped to produce the liquids, allowing the dry gas to build pressure in the wellbore. The duration of the PBU should have been about 1,000 hours to see the radial flow. Case 2: Surface-Shut-In DST With Downhole and Simulated Data A tight-gas-formation well was lifted with nitrogen at 300–500 scf/min from 5,000 ft to the bottom of the well with CT. Following the nitrogen lifting, the formation was matrix acidized with 10% greater than the wellbore volume at less than 0.5 bbl/min. The well was again lifted with nitrogen, producing a mixture of water and acid.
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Unconventional and tight-gas formation reservoirs present different challenges for testing compared with conventional reservoirs. Large changes in viscosity and compressibility for tight gas formations induce a long-duration WBS period.
The well was then shut in at the surface choke manifold using down-hole gauges for a 12-hour PBU. During the shut-in time, the wellhead pressure declined to zero with no nitrogen production at the surface. Fig.1 shows that the (Fig. 1) pressure increased by 4,800 psi, but the data do not show an IARF profile. The WBS dominated for 2 hours, and the remainder of the the PBU period was just a transition out of the WBS effects. In this case, the semi-log plot of the data was used to extrapolate and obtain an estimate of formation pressure and permeability-thickness product of 8,460 psi and 44 md-ft, respectively. In the figure, the green curve represents the actual pressure data collected during the pressure-drawdown and PBU periods, whereas the red curve represents
the derivative of the pressure data. A standard radial-flow model was used for the analysis of the collected data.
(Fig. 2: A)
with an average water production of 49 B/D. This well was then shut in at the surface using downhole gauges for 1,111 hours for a standard PBU to obtain reservoir pressure, permeability, formation damage, and productivity. The log-log, semi-log, and history plots of the pressure data provided in Fig. 3 show a long WBS effect with a long transition to bilinear flow and some evidence of the beginning of IARF. In Fig. 3, the green curve is the actual pressure data collected during the pressure-drawdown and PBU periods, whereas the red curve represents the match of the data with the model. After 46 days of buildup, the shut-in pressure increased from 1,471 to 8,970 psi at the pressure gauge depth that was 836 ft above the formation midpay.
(Fig. 2: B)
(Fig. 3: A)
(Fig. 2: C)
Fig. 2: A, B, and C are Log-log, semi-log, and simulation plot comparisons for both surface and downhole shut-in for Case 2. The simulation shows that in the event of a downhole shut-in, the end of WBS would have occurred two log cycles earlier, yielding the IARF regime to begin within 2 hours. Case 3: Well Testing of a Hydraulically Fractured Horizontal Gas Well with Surface Shut-In A 900-ft-long horizontal well in a tight-sandstoneformation gas reservoir in the Rocky Mountain region was hydraulically fractured. This well was located 40 ft from the bottom of a 64-ft-thick formation. During production, the pressure declined rapidly from 11,500 to near 1,900 psi in approximately 10 months and gas production declined from 2.5 MMscf/D to a stabilized flow rate of 1.07 MMscf/D for 221 hours,
(Fig. 3: B)
(Fig. 3: C)
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Case 4: Well Testing and Sampling with StraddlePacker WFT A naturally fractured carbonate reservoir was tested with wireline straddle packers by pumping out the drilling fluids from the formation into the wellbore until formation fluids were detected. Fluid samples were collected 3 minutes before shutting in the well for a 5-hour PBU. Fig. 4 provides the well log together with the pressure and the flow-rate history. (Fig. 5)
(Fig. 4—Well log and pressure- and flow-rate-history plots of data for Case 4. Downhole shut-in using a formation tester mitigates WBS effects and permits rapid testing of multiple formations. Well test in a single formation is presented here.)
The straddle packer exposed only 43 in of the entire 14-ft pay thickness. Thus, a partial-completion model with spherical flow was used to describe the flow geometry and analyze the PBU. Because of operational constraints, the formation was not allowed to flow sufficiently long after fluid sampling to provide production stability. The recorded pressure during the first 4 hours of the flow-cleaning period was higher than the analyzed model obtained from the PBU part of the data. This is an indication that during the drilling period, the higher-pressure wellbore fluids opened some of the natural fractures (from operator’s knowledge of this zone) around the wellbore which were closing with production. As the fracture was closing, the skin value increased and approached zero with production. Fig. 5 plots the increasing skin values during the cleanup period. The pressure-transient data matched a formation pressure of 2,078 psia, a permeability-thickness product k h of 3.21 mdft, k of 0.23 md, kx/kr of 0.547, and with a radius of investigation of 50 ft into the formation.
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Case 5: Formation-Property Evaluation With DFIT DFIT is an injection test conducted by pumping from surface or downhole to induce a mini fracture. Pumping stops after the formation is fractured, and the pressure falloff (PFO) is observed to obtain formation geomechanics and reservoir properties. Before hydraulic fracturing, an extended DFIT was conducted. This allowed the performance of afterclosure analysis (ACA) in addition to the typical geomechanically-parameter evaluation. The goal was to determine the breakdown pressure, closure stress, and the reservoir pressure to optimize the design and execution of a hydraulic- fracturing process. After mini fracture injection was stopped, pre-closure and after-closure analyses were performed using the PFO data. Using the traditional square-root-of-time plot or the G-function method, the pre-closure analysis provided information about the geomechanical parameters such as fracture closure. The ACA was conducted after the fracture had closed and provided reservoir properties such as pressure and mobility. Fig. 6 shows a DFIT operated from the surface, resulting in a closure pressure of 17,124 psia, an ISIP of 17,252 psia, a reservoir pressure of 13,590 psia, and formation mobility of 0.188 md/cp.
(Fig. 6)
(Fig. 6)
Case 6: Formation-Property Evaluation with Downhole DFIT WFTs have been used to perform micro-fracturing by injecting into a tight formation using either wellbore fluids or special fluids carried with the tool from the surface. With negligible WBS, the injection pressure is transmitted to the formation instantly, causing a rapid increase in the fluid pressure around the wellbore. Once this pressure increases to a level greater than the rock minimum stress, the formation fractures cause the fluid pressure to decline rapidly with increased surface area and higher fluid leak-off into the formation. Once the pump stops injecting, the fluid pressure quickly dissipates, causing the fracture to close. Because of the quick nature of fracturing and fracture closing, several injections and falloff periods can be performed with a WFT in a shorter time than when using a surface DFIT with one surface-induced mini-fracture followed by a falloff. Each cycle of fracturing and falloff can be analyzed separately and compared for added confidence in the analysis results. Fig. 7 shows a downhole micro-fracturing treatment performed on a shale formation using a WFT tool with four cycles of injection and falloff periods; Table 1 provides a summary of the formation rock mechanics properties obtained using the four microfracturing tests. The purpose of this micro-fracturing operation was to determine whether this shale barrier could withstand the high injection pressure into the sand below, which was planned for use as storage.
(Table 1)
(Fig. 7)
(Fig. 7) (Fig. 7 shows that the pressure profile during the fourth injection period was smooth and did not provide any fracture friction)
Conventional PBU testing has limited applications in tight-gas-formation wells. A DST should be conducted in unconventional gas wells only with downhole gauges and downhole shut-in. The only time a DST with surface shut-in can be used for low-permeability gas wells is when the well is also producing water and the wellbore is full of liquids. WFT, DFIT, and IFO are the preferred alternatives to a PBU test for formation testing and for pre-fracturing diagnostics in low-permeability formations. Nitrogen injection for the purpose of IFO testing is viable and can reduce the test duration in a tight-gas-formation well. It is the zero-emission equivalent to a flow/buildup test, without requiring the large surface-equipment footprint or flaring, which are typical in a DST operation. It has minimal formation-damaging effects and does not reduce the relative permeability to gas.
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Dr. Mohamed Basyouni
The Egyptian Red Sea Exploration and Hydrocarbon Prospectivty Senior Geologist, Upstream
The Red Sea is being presented as a new area for exploration within & NBD team at Dana Gas Egypt, after 12 years period where there has been no activity. The Ministry of Petroleum said on March 10 that it would accept bids by international oil and gas companies for exploration in ten spots along the Red Sea coast. The announcement came hard on the heels of the completion and processing of seismic data about the Red Sea coast. Oil and gas experts expect exploration in the Red Sea to open the door for an oil and natural gas bonanza for in Egypt. This optimistic forecast is compounded with positive developments in the Egyptian oil and gas sector following the discovery of what is to date the largest ever natural gas finding along Egypt’s Mediterranean coast. This article aims to undertake regional basin analysis study of the Egyptian Red Sea based on variable geophysical, geological, petrophysical, and geochemical data to demonstrate how we can help in better understanding the potential of the Red Sea basin. Study Challenges: - Only 12 wells drilled in the Egyptian Red Sea portion of the basin: all with some oil or gas shows. - Most wells were drilled on very poor seismic due to the presence of layered salt which is the main challenge for imaging. - Pre-rift play ‘Nubia’-’Brown’ Cretaceous - ’Thebes’– Eocene: 1) Main play in central GOS 2) Widespread deposition on Tethys Ocean passive margin, as south as north Sudan 3) Proven SR & Reservoir at outcrop in Quseir area the Red Sea hills 4) Not drilled in any Red Sea well Study Methodology: - How are the Red Sea basins formed? - Exploration history and what is different now? - Highlight of the technical overview.
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Region Potential: The Red Sea appears to have similar depositional history to the Gulf of Suez; thick salt and evaporites overlying rotated fault blocks containing lower and middle Miocene sediments. Well data in the Egyptian portion of the Red Sea is extremely sparse with only 15 (modern) wells in over 66,000 km2. Making an estimate of yet to find resources in the Red Sea necessarily has huge error bars associated with it. This is an unexplored basin immediately adjacent to the highly prolific Gulf of Suez which has produced 13bn bbls to date. The Red Sea is 7 times larger on the Egyptian side alone. The USGS made an assessment in 1998 that there was a YTF of 122Tcf + 5bn bbls oil. By any measure, the Red Sea has very material potential. Exploration History: There have been 2 previous exploration cycles: Quasir in the 1970s and Marsa Alem in the 1980s, when the majority of the 15 wells were drilled. Further activity continued on the Saudi side in this decade. Aramco discovered 3 oil and 2 gas fields in the northern Red Sea in 2013 and started developing the gas fields.
Principle obstacles to successful exploration: Although all the essentials appear to be present (structures, source rock, seals, and reservoirs), the extremely poor seismic image in the past has led to the poor location of the wells and resulted in exploration failure. A couple of exploration myths have persisted which have served to reduce exploration in the Red Sea: The area is not prospective as a result of high heat flow (adjacent to the oceanic spreading ridge). This is now dispelled by logged data which indicates that the geothermal gradient is very similar to the Eastern Med. The absence of sediments toward the center of the basin has been dispelled by the Nagel well which proved thick Miocene sediments close to the median line.
But now...
We have a better-quality seismic acquisition that raises the possibility of seeing a better structure. 12km cables in 2018 vs 3-6km cables in earlier cycles. We also have improved seismic processing techniques. The huge leaps in computing power and the variety of new PSDM techniques have substantially improved even the short cable 1980s acquisitions. Technical Overview: Formation: The Red Sea is a 2,000 km-long, NWtrending, continental rift system formed in response to the separation of the Arabian and African plates. It represents a modern example of an embryonic and young ocean basin formed by the continental break up. Excellent exposures in the Egyptian margin of the Red Sea provide splendid opportunity to study the rift structural, stratigraphic, and depositional architectures as well as evolutionary stages of the rift system. Play Concepts: The area includes two play concepts pre-rift play (Cretaceous Nubia sandstone, carbonate build-up at the top of basement & base of salt and fractured weathered basement). The second is syn-rift play (Belayim, Kareem, Rudeis & Nukhul sandstones, and Intra-evaporite sandstones). Source: The shales of Dakhla fm. (U. Cretaceous) and secondarily Rudeis (L. Miocene) is the oil and gas prone source rocks in the area. Dakhla is present in
the onshore but there is no evidence of its presence in the block. Rudeis has been penetrated by all wells drilled in the block but its quality is poor. The two source rocks are in the gas windows at the present time. Reservoir in the area: A) The pre-rift reservoirs, Nubia fm. (Carboniferous) or Thebes fm. (Eocene), are not present on the tructural highs as proven by the five wells drilled in the area, showing Miocene on a volcanic basement. The pre-rift reservoirs are most likely eroded on the uplifted highs, and they might be preserved in the lows like in the nearby onshore outcrops. B) Syn-rift (Kareem fm.) deposits of Early Miocene age are located in a pro-delta depositional setting which is characterized in the block by silty facies with poor reservoir quality. Traps: The additional potential is identified on shelf margin areas at the margin of the Red Sea, carbonate reef build-up plays are predicted at the pre-existing structural trend, similar to Ras Gharib and Amr fields at GOS The sub-salt structural traps represent the main trapping mechanism; stratigraphic traps cannot be excluded, even though they are more difficult to delineate and geologically riskier.
Based on this research and previous experience in the Red Sea area, it is ranked to two most attractive areas as follows: • North of Hurghada-Safaga: located nearby producing fields offshore/onshore southern Gulf of Suez from the Early Miocene sands and carbonates, close to sand fairways from northwest Red Sea the Cretaceous to Eocene pre-rift sediments. • Shalateen-Halayeb: at the southernmost end of the Egyptian Red Sea, this area is located along with a major sand supply of delta sediments derived from the River Nile distributary similar to the Tukar Delta in Sudan.
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Review
Relative rock properties: An alternative to estimate reservoir (or resource) properties from seismic data
Alvaro Chaveste
Chief Geophysicist at TraceSeis.
Estimates of reservoir properties porosity, lithology, and fluids using seismic data are customarily obtained through seismic inversion in two stages: in the first stage rock properties for example, (Pand S-impedances and density) are computed through elastic inversion and in the second stage the estimated rock properties are inverted to the reservoir properties of interest. Rock properties are computed from seismic data through elastic inversion. The process by minimizes the difference between observed data and data modeled through relationships that incorporate AVO, the offset-varying wavelet, and a Low-Frequency Model (LFM). Under this scheme inversion software searches for the rock properties that result in the synthetic data that best matches the real data, It is a mathematically complex process, the parameters in available software usually are not intuitive and the sensitivity of inverted properties to change in input parameters is poorly understood by the regular user. Parameterization is done, many times, by iteratively modifying parameters and comparing the inverted properties to their equivalent from well-logs. The LFM, required when inverting to absolute rock properties, provides the low-frequency component (including DC) on which the relative changes from seismic are superimposed. Its frequency bandwidth falls outside that of the seismic and it remains mostly unchanged during the inversion process. The LFM is created from non-reflectivity data, usually welllogs and seismic velocities, and its magnitude is several times larger than that of the relative rock properties’ changes measured by seismic. A small percent inaccuracy in the LFM can result in errors as large as the range of variation of the relative changes. Many approaches exist to estimate reservoir properties from inverted rock properties. Some are qualitative and based on defining in crossplots or multi-variate space the clusters of seismic attributes associated with a reservoir property as
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determined from equivalent properties from welllogs. Model-based approaches are deterministic and relate a reservoir property (i.e. porosity) to a rock property (i.e. impedance) using effective media relationships. Sometimes empirical relationships are obtained by fitting the inverted properties to well-log or core measurements of the sought for reservoir property at well locations. The obtained relationship is then applied to the rock properties in the 3D volume to obtain estimates of the desired reservoir property. These methodologies are analyst intensive and the reliability of results is strongly dependent upon the Geoscientist’s experience. The analyses to compute rock properties and the estimation of reservoir properties are often done by different Geoscientists, thus adding uncertainty to the estimated reservoir properties. A methodology was proposed in which relative rock properties (Ball et al., 2014) are used to compute reservoir (or resource) properties. It reduces user input and simplifies parameterization. In the proposed methodology each of the components of elastic inversion is done separately and their order of execution is modified. Under this scheme, the seismic wavelet is offset-equalized, and phase-corrected in the data conditioning stage, prior to computing relative properties. The LFM is incorporated, if necessary when computing the reservoir properties from relative rock properties. The methodology relaxes the need for a rigorous LFM and bypasses the estimation of absolute rock properties. Figure 1 compares the customary and proposed inversion flowcharts.
Review
Reservoir properties are computed as linear combinations of relative rock properties. Regressors of the linear inversion can be obtained from well-log or seismic data by least-squares fitting the reservoir property of interest through two or more relative rock properties. The LFM, when required, is input into the regression analysis as one of the properties through which the reservoir property is fitted. The LFM can be the lowfrequency expression of any property that mimics that of the reservoir property of interest. In many cases, for example, p-wave velocity from seismic is used as the LFM when computing total porosity. Figure 2a shows a quality control display for the estimation of the linear relationship. In this case relative Lambda*Rho and relative Mu*Rho, computed at the seismic resolution, are linearly combined to obtain a band-limited estimate of porosity. The last step in the proposed methodology is to compute reservoir properties in the seismic volume using the linear relationship estimated from well-log data. Relative properties from seismic are obtained by integrating (running sum). The reflectivities of the corresponding rock properties obtained through analytical transforms of AVO attributes (Ball et al., 2014). In the example shown (Figure 2b), porosity is computed from seismic through the linear combination of relative Lambda*Rho and relative Mu*Rho obtained in the welllog analysis shown in Figure 2a.
The proposed methodology computes, from seismic data, quantitative estimates of any reservoir or resource property that can be represented as a linear combination of relative rock properties. Effective porosity, brittleness, and mineralogy (including volume fraction of kerogen) are examples of properties that seismic data can be inverted to.
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Role of Uncertainty Analysis & Machine Learning in History Matching of Reservoir Simulation Models
Khaled Mansour
Senior Reservoir Engineer, Schlumberger SIS NME
History Matching of reservoir simulation models is the process of adjusting the model until it closely reproduces the past behavior of a reservoir, to be used to simulate future reservoir behavior with a higher degree of confidence. The accuracy of the history matching depends on the quality of the reservoir model and the quality and quantity of pressure and production data. Subsurface uncertainties stand against achieving history matching of simulation models, resulting in unreliable andunpredictable simulation models. This article discusses how to tackle uncertainties and the role of machine learning in history matching of simulation models.
First of all, let’s explore some typical uncertainties. Static Model Uncertainties examples include reservoir top structure and layering, gross formation thickness and net thickness, faults existence, facies types and distribution, and porosity and permeability distribution. Dynamic Model Uncertainties examples include fluid contacts, relative permeability, capillary pressure curves, flow units (rock types), residual oil saturation, PVT properties, aquifer behavior, and fault sealing/ transmissibility. The following figure provides a scheme for sampling techniques.
The first step of the workflow is to make a single simulation case based on your best knowledge of values of all input parameters. The base case contains all the parameters that will be defined as uncertain. Define And Calculate The Objective Function The objective function provides numerical parameterization of the optimization target used as a performance measure in optimization problems. It is used to compare the model quality between different simulation runs. In model history matching the objective function will be achieving the lowest mismatch between simulated and actual measurements (pressures, water cut, gas oil ratio, etc..).
where: s = simulated data, o= observed data, ω = weight, Ďƒ = measurement error, N = Number of points
(Figure 1)
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While in prediction scenarios the objective function will be achieving the highest oil production rate, highest production plateau, or highest NPV. Define the Uncertainty Matrix In this step, all the uncertain parameters and their ranges are defined in a table based on the knowledge of the reservoir, the availability of data, and the quality of data. The uncertain parameters are called variables.
Perform Sensitivity Study The main purpose of a sensitivity study is to understand the impact of each uncertain parameter, then ranking the uncertain parameters in order of their significance to reduce the problem complexity. Through “Tornado Plot�, the parameters are ranked according to their impact on a specific response (for example pressure, water cut, and gas oil ratio). The only drawback of sensitivity analysis is that it doesn’t consider the interaction between uncertain parameters and thus important parameters could be screened out unintentionally although they might be of great impact when combined with other parameters as discussed in the next section.
(Figure 2: Tornado Plot for Sensitivity Analysis)
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Perform Uncertainty Runs: Monte Carlo Simulation
After screening out the unimportant parameters from sensitivity, Monte Carlo kind of sampling is implemented to randomly sample values from the range of each uncertain parameter in each run, ie unlike sensitivity analysis all parameters change together in each run. This randomness is very essential to explore the impact of a different combination of uncertain parameters and study the interaction between them. The importance of running Monte Carlo to understand the interaction of uncertain parameters all together in each run. There are some enhancements to Mont Carlo sampler like Latin Hypercube which makes sure that Monte Carlo samples all the range of each parameter by dividing the range into equiprobable bins. Also, Orthogonal Array is an enhancement to Latin Hypercube and Monte Carlo because not only it samples all the range of each parameter but also takes into consideration the sampling of the whole search domain between all uncertain parameters together.
Assisted HM Through Machine Learning In the previous step, the randomness of sampling through Monte Carlo and was implemented. But randomness might not be enough to achieve a history match. The engineer needs the sampler (Monte Carlo) to learn how to pick values of uncertain parameters each run to achieve the best match or in other words the best objective function (lowest mismatch). For this purpose, an optimizer must be utilized, first the objective function calculates the mismatch between the first simulation case (or first cases from Monte Carlo) and the actual measurements (rates and pressures) to assesses how far the current simulation model/s from reality and rank them according to their match degree. In the next run the optimizer will try to pick new values for the uncertain parameters but this time it learns from the previous run/s which values could enhance the match between the model and reality.
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Genetic algorithm (GA) is one of the evolutionary algorithms and one of the basic machine learning algorithms in the oil and gas industry upon which AI is based. The GA workflow starts with “parents selection” from a pool of simulation cases that were previously created by Monte Carlo random sampler, the GA selects the fittest cases (i.e. has lowest objective function and lowest mismatch), then the GA performs “Crossover” of these parent cases to develop the next generation of simulation cases. Finally, the GA performs “Mutation” to the children cases in the next generation to further explore the search domain, by slightly modifying the values of uncertain parameters inherited from the parents, to make sure that the children cases are slightly different than the parent cases and thus could result in the lower objective function (i.e better history match) until best history match is achieved.
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Research
Application of a New IPR Model with a Comparison to PGOR Test and PLT Data to Evaluate Wells Productivity
Mohamed Elias
Senior Reservoir Engineer at Weatherford international.
In 2009 a new Inflow Performance Relationship (IPR) model was built using simulation and field data sets. Reservoir simulation was used firstly to accurately select the best fit between the oil mobility function and the average reservoir pressure. The new IPR was developed based on the resulted oil mobilitypressure profile. Then, many field cases were used to develop an oil mobility-pressure relationship. Accordingly, in this work, an attempt to apply the new IPR was proposed. To check the applicability and accuracy of the new IPR model, multi-rate test with bottom hole pressure is required to plot the actual IPR curve. Many Portable Gas Oil Ratio (PGOR) tests integrated with ESP bottom hole pressure data and Flowing Bottom Hole Pressure (FBHP) surveys were used to create actual IPR curve for each well. Also, bottom hole pressure and flow profile obtained by Production Logging Tool (PLT) data for each entire producing interval in a layered reservoir system is used to plot each actual IPR curve. Then, the new IPR model was tested and compared to the most common IPR models to get the IPR curve for each well as well. The applicability for different fields (KOC fields) of the new IPR model was tested and compared to the most common single point IPR models known in the industry (Vogel, Wiggins, and Sukarno). Many PGOR field cases were used for comparison. The best method for PGOR and PLT cases was the new method and that with average error of around 3.6 %, while the errors from the other models are higher than 4 % ranging from 4.5 to 5.3 % for PLT cases. To combine layered reservoir production testing, which could be conducted under pressure transient conditions with the inflow performance; it will help accurately to determine the optimum production rate for each well of interest. The results showed this method is accurate, reliable, and simple. Is general for oil reservoirs, requires only one test point and has a wide range of application. Introduction It was necessary to develop a new IPR model to accurately and simply estimate and get the inflow performance curves for oil reservoirs. simulations have shown that the main factor affecting IPR models is the oil mobility function and its relationship to the average reservoir pressure. The new developed model accounts for this relationship without any need for a direct field data measurement or calculations using simulation software. A reservoir simulation with many runs was used firstly to select the most accurate fit between the oil mobility function and the average reservoir pressure (Fig.1). The best fit was a reciprocal mathematical model with two constants a and b. Secondly, based on this model, Eq. 1 for the IPR starting from the pseudo steady state pressure was derived.
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(Eq. 1)
(Fig. 1)
Starting from this equation and using the reciprocal oil mobility-pressure assumption, a new mathematical IPR model was derived (Eq. 2).
Research (Eq. 2)
Alpha (Îą) was defined as (a/b) and named as the oil IPR parameter for the newly developed mathematical IPR model. It was necessary to use the simulation to get these constants (a and b) and the Alpha parameter in every case. To avoid this, many field cases were used to construct a relationship (Fig. 2) and in turn derive an empirical relationship between the oil mobility and the average reservoir pressure.
Table 1 (Fig. 2) Table 2
In order to increase the accuracy of the curve fitting process that was used to obtain the correlation that relates the Îą-parameter with the average reservoir pressure, the resulting chart (Fig.2) was divided into two ranges as described below. Part one, if the average reservoir pressure and/or shut in pressure is less than or equal to 1600 psia, a reciprocal equation (Eq. 3) was obtained: (Eq. 3)
Part two, if the average reservoir pressure and/or shut in pressure is greater than or equal to 1600 psia, a fifth polynomial equation (Eq. 4) was proposed. Finally, Eq.2, Eq.3, and Eq.4 represented the new developed IPR model. (Eq. 4)
PGOR and PLT Applications The main objective was to check the accuracy and reliability of the new IPR model against PGOR and PLT field data. In addition, these results were compared to the most known and used IPR equations of Vogel, Wiggins, and Sukarno which are all singlepoint models. Tables 1 and 2 show the details of the 24 PGOR and PLT cases, which were collected from different reservoirs in Kuwait. A wide range of reservoir conditions, reservoir pressures, shut in pressures, rock properties, and fluid properties were applied as summarized in Table 3.
Table 3
PGOR Case No.X3 Table 4 shows PGOR tests over 5 years combined as multi-choke portable GOR test data for a well producing from a South East Kuwait field. The average reservoir pressure was 1891 psi in a saturated reservoir with assumed zero skin factor. Table 5 is introducing the calculated inflow curves at a flowing bottom hole pressure of 1604 psia (15% pressure drop from the initial reservoir or shut in pressure).
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Research The Absolute Open Flow (AOF) ranges from 5435 to 7707 STB/D. The highest AOF value was obtained by Wiggins’s IPR, and the lowest AOF was predicted using Elias model. Fig.3 is presenting the different calculated IPR trends against the PGOR test data set. The new proposed IPR model is the most accurate to match the field data set. Furthermore, it is apparent that the newly developed IPR model and Sukarno’s model are more accurate at estimating the AOF for this well than the other models, and as shown, the other models overestimate the AOF value.
Table 6
Multi-Choke PLT- Case X24 Table 7 presents Multi-Choke PLT data under two different choke sizes from a SEK field. The shut-in pressure was measured at 1370 psia in a free gas cap reservoir with an assumed zero skin factor.
Table 7
Table 8 is introducing the calculated inflow curves at a flowing bottom hole pressure of 1296 psia (5.4% pressure drop from the initial reservoir or shut in pressure). The AOF ranges from 21087 to 50040 STB/D. The highest AOF value was obtained by Wiggins’s IPR, and the lowest AOF was predicted using the Elias model. Fig.4 is presenting the calculated different IPR trends against the PGOR test data set. The new proposed IPR model is the most accurate match to the field data set. Furthermore, it is clear that the newly developed IPR model and Sukarno’s model are more accurate at estimating the AOF for this well than the other models, and as shown, the other models overestimate the AOF value.
Table 4
Table 5
(Fig. 3)
Table 6 shows the average errors relative to the PGOR data in the predicted rates for all IPR models that were used in this study for this case at all pressure drawdowns. The Elias IPR model has the lowest average error at 3.8 %, while the average error for Vogel’s method is 4 %. The other singlepoint methods have average errors ranging from 3.86 to 4.11 % for Sukarno and Wiggins, respectively.
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(Fig. 4)
Research The average error relative to the Multi-Choke PLT data in the estimated rates for the four IPR models are shown in Table 9 at all pressure drawdowns. It is obvious from the table that the Elias model has the lowest error at 0.10 %, while the average error for the other models is 3.0%.
Table 9
Application Summary As clearly demonstrated, the Elias IPR model accurately predicted the inflow performance curves in almost all of the cases with the lowest error at 3.6 %. The most effective relationship to predict the well inflow performance is the relationship between the oil mobility function and the average reservoir pressure. That relationship was addressed in an accurate way mathematically by assuming a reciprocal relationship to the fit between the oil mobility function and pressure, it was then shown again empirically by using multiple field cases to obtain the Alpha parameterpressure relationships. In turn, the accuracy and reliability of the Elias IPR model is significantly better than the most known IPR models in the oil and gas industry. An important point in this study is the field data selection, in which the geology of Kuwait and its inherent formation properties are very interesting in the application of any new IPR model for production optimization. For example, the absolute permeability range is up to 20000 md (20 Darcy) which is considered an extremely high permeability value for sandstone. In addition, the formations are stratified, very thick at up to 2000 ft, and medium to clean in shale volume. The high value of permeability increases the challenge for any IPR model to predict the inflow performance accurately. In addition, the absolute permeability with fluid saturations affects the relative permeability data. As discussed, the most affective reservoir relationship for any IPR is the one of oil mobility-pressure, which is mainly a function of relative permeability data, oil viscosity, and oil formation volume. The use of Multi-Choke PLT is very important point here and adds value to the study, in which the PLT data gives the opportunity to predict the selective zone IPR curves, especially with the integration of a simple and accurate IPR model. All the selected PLT wells have more than three producing zones, in this case for each well; SIP curves prediction was addressed and studied in a simple and accurate manner. The results of the PLT cases X23 and X24 are very interesting because the average errors using the Elias model are 2.0 % and 0.1 % respectively. Therefore, the chance to have accurate and reliable SIP curves using the Elias model is much higher than the other IPR models used in this study. Finally, for the 24 cases, the models of Vogel, Wiggins, and Sukarno gave higher average errors ranging from 4.5 to 5.3 %.
In this study, the main objective was to check the reliability of the new IPR model of Elias under different reservoir and production conditions. That was done by applying the model in different fields which have a variety of property ranges especially rock and fluid properties. The most common IPR models were used for comparison in the 24 field cases of PGOR and Multi-Choke PLT data. Based on this study, we can conclude the following: 1. In a simple and accurate way, the new IPR model from M. Elias can be applied for an extremely high value of absolute permeability up to 20 Darcy. 2. The new IPR model from M. Elias can be simply and accurately applied under a wide range of reservoir rock and fluid properties especially relative permeability data. 3. The new IPR model is very useful and accurate to predict the inflow performance curves for stratified and thick formations (more than three zones with thickness up to 2000 ft). 4. The new IPR model is the best model that accurately constructed the IPR curve for the studied field cases using a single test point. 5. It is not recommended to use the new IPR with reservoir pressure less than 800 psia as this range is not covered in the alpha parameter pressure chart (Fig. 2). 6. This new IPR model assumes a zero-skin factor as do most IPR models.
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CALCULATING BRITTLENESS INDEX USING TRIPLE COMBO LOGS Advanced curve fitting techniques have been used to find the local correlations between the available triple combo well logs and the brittleness index.
Dr. Ahmed Algarhy Assistant Professor at Marietta College
The area under study is in the western desert of Egypt, south of the Mediterranean Sea. The well is slanted and reaches a measured depth of 14,248 ft. The main objective is establishing an accurate estimate of brittleness index for this organic shale formation and ultimately help in designing a better hydraulic fracturing stimulation job and determine a better depth for the laterals.
Raw Data Before building a model for the brittleness index, data collection, data review, and data quality assurance should be completed. Overview of the well data - Total depth = 14,248 ft. - Top of Formation under study = 12,756 ft. - Formation thickness = 1032 ft. - Total core length is 311 ft. in Khatatba organic shale. 227 ft. in top of Khatatba shale and 84.1ft. in the bottom of Khatatba shale formation. Collected data • Triple Combo (LWD) • Geochemical log for shale mineralogy • Imaging for lamina and fractures/stratigraphy • Cross dipole sonic • Dielectric and nuclear magnetic resonance for saturation and free fluid porosity • Reservoir description tool (RDT), pressure measurement for trapped sandstone thin layers • Preliminary evaluation Comparison Between Rickman and Jarvie Brittleness index using Rickman and Jarvie approaches have been calculated and plotted in Figure 1. It is obvious that at some depths (from 12700’ to 12800’ and 12850’ to 12900’) where Rickman (using mechanical properties) and Jarvie (using chemical properties) show incomparable results. Geochemical log data of those depths show the presence of high percentage of Calcite. In Jarvie’s brittleness index the calcite term remains in the denominator that result in low brittleness index. On the other hand, Rickman equation uses Poisson’s ratio and Young’s modulus which is, in fact, higher for calcite zone.
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(Figure 1)
(Figure 2)
Calculating Brittleness Index Using Bulk Density, Neutron Porosotiy, Gamma Ray, and Resistivity In this section we use the normalized resistivity as follows:
where Rnr is normalized deep resistivity, Rmax is the maximum reading of deep resistivity, R is deep resistivity reading at that corresponding depth, and Rmin is the minimum deep resistivity reading
In this case (Figure 2), the new brittleness index gives a good match with Rickman’s equation. The calcite problem is less severe when the resistivity is considered in the new brittleness index. The match is acceptable in most of the intervals. Comparing the new brittleness index correlation with Jarvie shows poor match as shown in Figure 3. As mentioned before, the reason for that mismatch is the fact that when we created the new correlation of brittleness index we considered Rickman is the correct reference.
(Figure 3)
Coring multiples wells for geochemical analysis is an expensive and time-consuming operation. Coring a few wells during the evaluation stage of a shale play can help to build local correlations to estimate the brittleness index based on triple combo well logs only. The accuracy of estimating the brittleness index by using triple combo is acceptable. The correlations used to calculate brittleness index will vary with the locations. This means some lab data for few cored wells at that location is required for correlation validation purposes. 33
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BP starts drilling gas exploration well in North El Tabia offshore block BP began drilling its Atoll Nooros exploratory gas well in its North El Tabia block in the Mediterranean. The company will complete drilling in four months and reveal results in 2H2020. BP had signed an agreement with the EGAS in 2016 to start exploration operations in the North El Tabia offshore block area with investments of USD 65 milion.
Egypt’s 2020-2021 draft budget is based on $61 oil barrel price Egypt’s 2020-21 draft budget is based on an oil price of $61 per barrel, the finance ministry said, down from $68 in the current budget which ends on June 30 but around three times higher than the present price. Oil prices fell sharply, with U.S. crude briefly dropping below $20 and Brent hitting its lowest in 18 years, on heightened fears that the global coronavirus shutdown could last months and demand for fuel could decline further.
Natural Gas Regulatory Authority lowers fees for commercial licenses
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The Natural Gas Regulatory Authority will charge companies lower fees this year for commercial gas transmission, shipping, distribution, and supply licenses, Al Mal reports, citing an unnamed top government official. The new rates are between 22-30% lower.
Pharos Energy confirms its interest in Shell’s Egypt upstream assets Pharos Energy has confirmed its interest in purchasing Shell’s onshore oil and gas assets in Egypt’s Western Desert as part of a consortium, and has said in a filing to the London Stock Exchange it is in the initial stages of evaluation. The interest doesn’t necessarily guarantee that a proposal will be made to Shell or that a transaction will take place, the company said.
Shelf Drilling Secures Contract Extension in Egypt Shelf Drilling, Ltd. (SHLF) announced that it has secured a one-year contract extension for the Trident 16 jack-up rig in direct continuation of its current term with Belayim Petroleum Company “Petrobel” for operations in the Gulf of Suez offshore Egypt. The Trident 16 has been working with Petrobel in Belayim fields since 2015, and following this extension, the expected availability of the rig is February 2021. PETROPULSE
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Total, Shell Announce Multi-billion-dollar Budget Cuts as Oil Continues Decline
Total and Shell each introduced significant cost-reduction measures, as the oil price war and the global spread of Covid-19 combined to disrupt operations. Following the lead of other supermajors like BP and ExxonMobil, Total and Shell are implementing plans to reduce capital expenditures, operational costs, and cancel planned share buybacks.
Total Tallies New Gas, Condensates Discovery in UK North Sea Total and its partners have made an encouraging discovery with the Isabella 30/12d-11 well on the license P1820, located in the Central North Sea offshore U.K. The well was drilled in a water depth of about 80 meters and encountered 64 meters net pay of lean gas and condensate and high-quality light oil, in Upper Jurassic and Triassic sandstone reservoirs.
Storing Crude at Sea Becomes a Profitable Option for Traders The latest collapse in oil prices has put in play a lucrative trade to store crude at sea, reviving memories of the 2008-09 recession when millions of barrels were kept on the world’s oceans. Brent crude in May 2020 is now trading at about $14 a barrel more than it is in May of this year. That constitutes a premium of $28 million over a year for a standard supertanker cargo holding 2 million barrels. A trader can profit from floating storage by selling cargoes in the future, provided the premium they’ll fetch for it would exceed the cost of hiring a tanker.
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Oil War Hits Home as Saudis Cut Nearly 5% from National Budget
Saudi Arabia announced 50 billion riyals ($13.3 billion) in budget spending cuts after the crash in oil prices and the coronavirus outbreak wreaked havoc on its public finances. As the kingdom doubled down in its price war with Russia, authorities signed off on expenditure reductions equivalent to under 5% of the total outlays approved in this year’s budget.
Saudi Arabia Pumps 12 mmbbl for the First Time Saudi Arabia’s oil production exceeded more than 12 million barrels per day (mmbbl/d) for the first time in the Kingdom’s history. The previous production record of KSA was 11 million barrels of oil. Officials at Aramco told Arab News that production on April 1 had even surpassed 12 mmbbl/d. The company released a short video showing laden oil tankers sailing away from Saudi ports,reporting that it had loaded 18.8 million barrels onto 15 tankers. PETROPULSE
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