Abstracts and Proceedings of the Geological Society of Norway

NGF

Abstracts and Proceedings of the Geological Society of Norway

Number 4, 2013

Production Geoscience 2013 Can experiences from mature fields help “New Field development”?

Stavanger, November 6th, 2013

www.geologi.no

© Norsk Geologisk Forening (NGF), 2013 ISBN: 978-82-92-39484-7 NGF Abstracts and Proceedings NGF Abstracts and Proceedings was first published in 2001.

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NGF Abstracts and Proceedings of the Geological Society of Norway Number 4, 2013

Production Geoscience Can experience from mature fields help "New field development" Stavanger, November 6th, 2013

Editors: Alister MacDonald, Lundin Berit Forbord Moen, NGF

Programme committee: Chairman: Marian NĂŚss Haga, Statoil ASA Ragnar Knarud, Statoil Cato Berge, Shell Erik Wulff-Pedersen, RWE Alister MacDonald, Lundin

Sponsors and supporters: NGF gratefully acknowledges support from the following:

Conference sponsors:

Conference exhibitors:

Contents AbstractS: Blixt, E. M., Paul Tijink, P. Well test simulation: Analysis of well tests in new fields utilizing experiences from mature fields ....................................... 4 Cense, A., Noraberg, K.-T., Krassnitzer, M., Johnson, L. Constraining geological models with dynamic data using experimental design workflows in the Ormen Lange Field ............ 6 Chazy, A., Saure-Thomassen, A., Grünhagen, H. Combining Production Logging and Well Data – A Marriage in the Seismic Wilderness ................................................ 8 Halset G.M., Gustafsson L.E., Rommetveit B. Reducing Geological Uncertainties in the Early Development Phase, Goliat Field ............................................................ 10 Harstad, A.O., Skjærstein, A. The Zidane Field – Moving towards PUD ................................................................................................................................... 13 Ildstad, K. Norwegian Continental Shelf (NCS) – Learning from History ......................................................................................... 15 Khataniar, S.K. Deployment of an uncertainty analysis workflow to support concept selection – case study .............................................. 16 Kråkenes, T. Reservoir Modelling – Some key Challenges and Technical Solutions ............................................................................... 18 Leutscher, J. Goliat Field, a step by step improvement of the seismic image ........................................................................................... 20 Rutledal, H., Hatland, K. Oseberg Field – A ground-breaking history ....................................................................................................................... 23 Smedal, R. Lessons learned from existing mature fields GULLFAKS: NEVER SAY NEVER! ............................................................ 26 Sønneland, L. Geological knowledge transfer in production workflows – new opportunities ................................................................... 28

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Well test simulation: Analysis of well tests in new fields utilizing experiences from mature fields Erik M책rten Blixt 1 and Paul Tijink 2 1) Blueback Reservoir AS 2) DNO International

Finding an optimum field development plan depends critically on reservoir characteristics such as aquifiers, fault transmissibility and reservoir permeability.

Well test are known to give estimates on Kh product (effective permeability), reservoir boundaries and faults.

Can well tests in history matching be of help in estimating the critical reservoir characteristics? History matching is an underdetermined, and thus ill-posed, problem, can we use experience from well tests on previous mature developed (analog) fields to partially rectify this, and help us quantifying these critical estimates?

Drawing an analogy from mature fields to new fields must be done carefully depending on some critical factors, for example reservoir thickness. In thin reservoirs the fault transmissibility will have a much larger impact than in reservoir with significant thickness (Edwards, 1988).

Capacitance-Resistive Models are analytical methods that give fast predictive results of the fault-transmissibility based only on injection and production data only, and not on reservoir geology. A semi-analytical extension incorporates the pressure data in to the history matching process to gain a more quantitative estimate of the transmissibility (Bansal, 2012).

Figure 1. Outline of the suggested history matching strategy, in which the reservoir model is updated so that the simulated pressure difference and derivative is matched with the observed pressure transient.

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Figure 2. A typical example of pressure transient behaviour in a build-up from observed data. The crosses represents the pressure difference (Δp), while the circles the pressure derivative (∂p).

The section before the first vertical bar yields information about wellbore storage. If the simulated data differs significantly from the observed in this section the wellbore storage has not been properly modeled. The plateau in ∂p between the two vertical bars yields information about the Kh product, while the distance between ∂p and Δp is an estimate of the skin. The fact that ∂p continues to decrease towards the very end of the time series is indicative of that the reservoir boundaries have not had time to interact with the data.

To more properly involve the reservoir geology in to the permeability model one can scale the permeability model based on the interpreted Kh product from well tests. However, this doesn’t answer the question of which term in the Kh product that contributes the most. The inhomogeneous nature of the permeability, K, further complicates the investigation.

We therefore propose a method which involves history matching both the pressure change and pressure derivative from a well test. The shape of these curves is an implicit representation of the characteristics of the reservoir (magnitude of K, heterogeneity, proximity of boundaries).

The idea is that this approach allows you to capture the reservoir characteristics in the 3D model in a much more robust manner. Hence, if you match these shapes you have a much better chance of getting the 3D model right.

Doing a history matching analysis on pressure change and pressure derivative from well tests is a labor intensive exercise. Making the reservoir simulator report at logarithmically distributed time steps, and exporting the simulation results to software suitable for analyzing the simulated pressure derivatives and comparing it to the derivative of observed data is complicated. Blueback Reservoir AS have therefore developed a plug-in to Petrel, that makes it possible to do the whole workflow inside Petrel, except from some initial pre-processing of the observed pressure data.

In this paper we are using the proposed method of history matching the pressure change and pressure derivative from a well test on a series of data from new and mature fields, utilizing the newly developed software to streamline the work.

There are many apparent difficulties in making analogies from mature to new fields, but it has recently become clear that well tests in a new fields (well clean-up) provides valuable base-line data for the future performance of the field (Whittle, 2013). Thus, in cases where it is difficult to use analogies from mature fields, the initial tests on a new field may become similarly important in the further development design.

Works Cited Bansal, Y. a. (2012). Fault-Block Transmissibility Estimation Using Injection and Production Data in Waterfloods. SPE Annual Technical Conference and Exhibition. San Antonio: Society of Petroleum Engineers.

Bourdet, D., Ayoub, J., & Pirard, Y. (1989). Use of Pressure Derivative in Well Test Interpretation. SPE Formation Evaluation, 293-302.

Edwards, K. a. (1988). Use of Well Test Results in Oilfield Develop­ ment Planning in the Timor Sea. Journal of Petroleum Technology, 40(10), 1372-1382.

Weber, D. (2009). The Use of Capacitance-Resistance Models to Optimize Injection. Austin: The University of Texas at Austin.

Whittle, T. (2013). Personal communication. -: -.

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Constraining geological models with dynamic data using experimental design workflows in the Ormen Lange Field Arjen Cense, Knut-Terje Noraberg, Michael Krassnitzer, Lance Johnson A/S Norske Shell

The Ormen Lange field is one of the largest gas fields in Norway, lying 120 km west of Kristiansund. It was discovered in 1997 and first gas was produced in 2007.

Currently the field produces approximately 25% of the UK daily gas consumption. The presentation focuses on how we use dynamic data, in particular production data and pressure data from the wells, to constrain possible geological scenarios. By applying an ‘experimental design’ workflow, we show how uncertainties impact our models. This helps us to focus on the most important uncertainties and to design strategies to address these uncertainties.

Location of the Ormen Lange field 125 km northwest of Kristiansund. Inset shows contour lines of the top of the reservoir, where the Direct Hydrocarbon Indicator (DHI), shown in green, indicates present or past gas-water contacts.

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Frøya High

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N Conceptual depositional model for the Ormen Lange deep-water fan system. Sand-rich distributary systems evolved through progradation into a series of linked slope depocentres created by underlying extensional faults. Sediment transport direction was influenced by the orientation of the available accommodation, which is locally at a high angle to the regional dip direction. Local incision and bypass may have occurred on steeper slope ramps which experienced spill from the portions of the fan confined up-dip.

A south-nord oriented structural cross-section of the Ormen Lange field with a projection of the mapped seismic DHI and a reconstructed free water level (FWL) surface. The FWL shows local variation of its geometry, but on average defines an upper bound of northward thickening prism of residual gas, identified in most of the field wells.

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Combining Production Logging and Well Data – A Marriage in the Seismic Wilderness Annick Chazy, Anna Saure-Thomassen and Henrike Grünhagen Statoil, Bergen, Norway

KVB is a gas condensate HPHT field, where production started in 2004. Data acquisition in the wells had then high focus (Data acquisition document in 2006 of 53 pages), with image logs and pressure data (among others) taken in (almost) all the wells. It was also planned for comprehensive production logging: 22 PLTs should be logged by 2010 (31 over the field life time). But actually, only 10 PLTs were conducted by 2008. Delay in the drilling schedule and depletion increase cast a shadow over PLT logging. Too high cable friction due to depletion lead to the termination of two PLTs and to the comprehension that PLT on KVB was unfeasible.

The seismic image is very poor on Kvitebjørn due to the depth (4000+m) and several overburden effects, resulting in huge structural uncertainties and consequently challenges in history matching the models resulting in huge structural uncertainties and consequently challenges in history matching the models. The increasing need for understanding the field’s dynamics, the availability of

a low-friction PLT technology, stubborn engineers and a probable stop in the drilling schedule resulted in the approval of 5 PLTs, which were performed in 2011 and 2012.

The PLT data was coming in at a time when the geologists were busy re-interpreting the zonation in and around some wells, using thickness statistics, image logs and RFT pressure data. It turned out that the PLT data could be used as a valuable peace in the subsurface puzzle, adding the essential spatial aspect to the local well data. This effect was even increased if several wells were logged subsequently, giving valuable data about the communication between them.

To simplify the analysis, the KVB PTC team developed a new way of visualizing the PLT data together with other available well data, which resulted in a successful multidisciplinary interpretation.

Non electric cable (slick line)

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in:

This presentation will show in details how it resulted • A re-perforation job giving immediate 50 % increased gas rate in the well • Large changes in the structure model • Valuable information about the risk of depletion at a well target, thus changing drilling method and avoiding HMS issues

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With low cost and high return on investment, the PLT operations and subsequent interventions were a success in itself. The benefit from the improved geomodel/ simulation models is hard to quantify, but brings the value of static AND dynamic data acquisition to a priceless level. This story shows that PLTs are not only a reasonable activity on KVB, but that they are critical and contribute to a high potential production increase and are worth occupying rig time for.

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Reducing Geological Uncertainties in the Early Development Phase, Goliat Field Halset G.M., Gustafsson L.E., Rommetveit B. Eni Norge AS

Introduction The Goliat field is part of the production licence 229, which was awarded in May 1997. It is located in block 7122/7 in the SW part of the Barents Sea, 85km NW of Hammerfest. The licensees in PL229/229B are Eni Norge AS (operator, 65%) and Statoil Petroleum AS (35%).

The Goliat field will be the first oil producing field in the Barents Sea, and production start-up is planned to take place fourth quarter 2014. Estimated recoverable oil reserves is 28 million Sm³/174 million barrels and recoverable gas reserves is 8 billion Sm³. The field is expected to be in production for at least 15 years.

The field consists of two separate reservoirs; the Kobbe Formation, at approximately 1800 meters depth and the Realgrunnen Subgroup (Tubåen Fm and Fruholmen Fm), at approximately 1000 meters depth. Both reservoirs contain oil with an overlaying gas cap.

The Realgrunnen Subgroup reservoir was deposited during Late Triassic in a proximal fluvial environment with some tidal influences. The Kobbe Formation of Middle Triassic age represents essentially a prograding deltaic system with mouth bars and tidally influenced lobes. In its lower section, the system shifts into a more proximal heterogeneous fluvial setting.

Both reservoirs share a complex structural setting characterized by a high number of faults. Seismic interpretation, formation pressure analysis and geochemistry analysis point to a complex field with several compartments and fluid barriers. Realgrunnen is divided into three segments; Central, Main and South. Kobbe is divided into four segments; Central, Main, South 3 and South 4.

Five exploration wells and one sidetrack were drilled before the development drilling campaign started in October 2012. The first exploration well (7122/7-1) discovered oil in Realgrunnen Main compartment, the second well (7122/7-2) discovered oil in Realgrunnen

Central and the third well (7122/7-3) found gas and oil in Realgrunnen South and oil in Kobbe South. The fourth well (7122/7-4S) found oil and gas in Kobbe and the fifth well (7122/7-5) found water in both reservoirs, but was sidetracked into the Kobbe Formation, proving oil in the Kobbe Central segment. The PDO was approved in 2009.

In the development phase a total of 22 wells; 11 oil producers, 2 gas injectors and 9 water injectors are planned to be drilled from 8 templates. The development drilling campaign started with one appraisal well, 7122/76, and by June 2013 one pilot and three water injectors were drilled from the H-template (Figure 1).

Uncertainties In the PDO a comprehensive uncertainty study was performed including both static and dynamic parameters, all combined together to give a probability distribution of STOOIP, GIIP and reserves. A total of 8 parameters were investigated isolated and combined; Vertical and horizontal permeability (Kv/Kh), Relative permeability, Size of aquifer, Faults, Fluid contacts, Structure, Facies iteration and Net to gross (NTG). This presentation will focus only on the parameters considered as static, being the uncertainty in the structural model and depth conversion, fluid contacts, facies distribution and NTG. The influence of the uncertain parameters was compared, showing that the Kobbe oil in place was mostly influenced by structure and contact uncertainties, while the Realgrunnen oil in place was mostly influenced by the structure uncertainty (Figure 2).

Reducing uncertainties In 2009 a multiazimuth Q marine seismic survey was acquired with improved results compared to the previous acquisition. Based on the seismic, new models were built; PSTM model in 2011 and PSDM model in 2012 - resulting in three different structural models; PDO, PSTM and PSDM, for both reservoirs. All three models where used for well planning, but after drilling

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Figure 1 Top Realgrunnen map (left) and top Kobbe map (right) with drilled wells and proven hydrocarbon contacts

the appraisal well and the H-template wells the aim was to establish one base case model for each reservoir.

The first well in the drilling campaign, 7122/7-6, was drilled as an appraisal well, in order to reduce the most important uncertainty in the field; hydrocarbon contacts in the undrilled Kobbe Main segment. A comprehensive data set was acquired for both reservoirs, including a full set of LWD (logging while drilling) data and WL (wireline

logging) data. The LWD data consist of the standard logging set (Gamma ray, Resistivity, Density, Neutron, & Acoustic) for both reservoirs. In addition the Nuclear Magnetic Resonance (NMR) was acquired for the Kobbe Formation. The WL data consist of the standard logging set (Spectral Gamma ray, Resistivity, Density, Neutron & Sonic). In addition the NMR, Image log (FMI), Borehole seismic (VSP) and the Modular Formation Dynamics Tester (MDT) were acquired for the Kobbe Formation. Both formations were cored (Realgrunnen 37m and Kobbe 28m) and fluid sampled (Oil & Water in Realgrunnen and Gas, Oil & Water in Kobbe).

The next well and the first well on the H template, well 7122/7-H-1H, was drilled as a pilot well with the main purpose to define the segmentation of the Kobbe Formation. Uncertainty existed about the definition of the Kobbe Main compartment, the Kobbe Central compartment, and the potential of additional segments in terms of hydrocarbon contacts. Also for this well the standard logging sets of LWD & WL data were acquired for both formations. In addition the NMR, FMI and the MDT (only for the Kobbe Formation) were acquired with WL. Very important information of structure, facies and fluid contacts within both reservoirs were achieved.

Figure 2 Results from uncertainty study performed in the PDO work. Tornado plots comparing the influence of the static uncertain parameters on the STOOIP

The second well on the H-template, well 7122/7-H-3 H was drilled as a water injector in Realgrunnen Subgroup Main compartment. The well was placed about 150 m N-NE of the exploration well 7122/7-1. This well result gave important information about the NTG and zonation in Realgrunnen. The standard logging set of the LWD data was acquired for this well. Due to the high deviation of this well, no WL data was acquired.

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Well 7122/7-H-4H is a water injector well for the Kobbe Main segment and was drilled as the third well on the H-template. Standard logging sets of LWD & WL data were acquired for both formations. In addition the NMR (LWD & WL), Image log (LWD) and VSP (WL) for the Kobbe Formation were acquired. The results improved the structural control of the northern flanks as well as NTG and facies distribution in both reservoirs.

The last well on the H-template, well 7122/7-H1AH, was drilled as a water injector in the Kobbe Central segment. The results from this well were as expected, in line with the earlier drilled pilot well 7122/7-1H (700m NE). The standard logging set of the LWD data was acquired for this well in addition to NMR, Image log and formation pressure (Testrak). Due to the high deviation of this well, no WL data was acquired.

Status after first phase of the drilling campaign • PSDM structural models are the most reliable • Kobbe Main segments has hydraulic communication towards Central segment • Oil water contacts have been revised according to new well observations • New well data and sedimentological study of cores from well 7122/7-6 confirmed the conceptual models, both for Realgrunnen and Kobbe • New well results increased the NTG in Realgrunnen and gave useful information about facies distribution for both reservoirs • Realgrunnen seems to have a more complex erosion than expected, resulting in a different internal zonation • Petrophysical properties, i.e. porosity, permeability and water saturation found in the new wells were in line with the modelled properties

Summary and conclusion Goliat is a heterogeneous and structurally complex field which went into the development phase realizing major uncertainties remained unresolved. The drilling schedule and the data acquisition were designed to systematically reduce the uncertainties and hence establish the best possible basis for the subsequent well planning.

No unforeseen uncertainty factors have been identified during the post PDO drilling. Crucial information about hydrocarbon contacts were the most important information achieved in the early drilling phase. The conceptual depositional models have been confirmed, but reservoir zonation and facies proportions and distributions have been adjusted according to new well observations.

The most important remaining uncertainties are the fluid contacts in South 3 segment in Kobbe, OWC Realgrunnen South segment, NTG and facies distribution in addition to structure on the south flanks, which is not penetrated by any wells yet. These uncertainties will be reduced by the next wells on the drilling schedule.

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The Zidane Field – Moving towards PUD Andreas Olaus Harstad and Anne Skjærstein RWE Dea Norge AS, PO Box 243, N-0213 Oslo, Norway

The Zidane Field in the RWE Dea Norge operated PL435 was discovered in 2010 when the exploration well 6507/7-14 S proved gas in reservoir rocks of the Middle Jurassic Fangst Group. Production license 435 containing the Zidane Field is located on the edge of the Halten and Dønna Terraces, in the Norwegian Sea between the Victoria discovery and the Heidrun Field (Figure 1).

The current license ownership is; RWE Dea Norge AS (40%), Edison International S.p.A. Norway Branch

(20%), Maersk Oil Norway AS (20%) and OMV (Norge) AS (20%). Prior to the activities associated with PL435, one exploration well had been drilled on the Zidane East structure. The well 6507/7-01 was drilled by Conoco Norway Inc in 1984 and located in a more down-flank position, some 3.7 km north east of the Zidane discovery well (6507/7-14 S). The 6507/7-1 well targeted the Middle Jurassic sands of the Fangst Group and was classified as dry following a production test where formation water with only minor gas (less than 28 300 Sm³/d) was produced from the upper Garn Formation.

Despite the negative results of the 6507/7-01 well RWE Dea Norge evaluated the prospectivity of the lisence as significant and PL435 was awarded following the 2008 APA round. The obligatory PL435 work commitment in the initial phase consisted of the following: • Within 2 years: Reprocessing 3D seismic interpretation • Drill or drop decision end of second year • Within 4 years: Drilling of one exploration well • Decision to continue (BOV) • Within 5 years: Submit PDO or relinquish

Figure 1 Overview map showing the position of PL435 holding the Zidane discovery and the surrounding oil and gas fields.

Reprocessing of 3D seismic data survey RD07M1 was completed in April 2008. A decision was made to enter the drill phase and the exploration well 6507/7-14 S was drilled using the rig Bredford Dolphin. The well was spudded on the 14 June 2010 and by 26 September 2010 all operations were completed. The 6507/7-14 S well proved commercial volumes of gas in the Garn and Ile Formations in the Zidane East segment. Following detailed data analysis and subsequent evaluation of the Zidane discovery well, it was decided to go forward and explore the hydrocarbon potential in the second Zidane segment, the Zidane West segment. Based on the success of the 6507/7-14 S well, it was concluded that improved seismic resolution was important in order for the project to progress toward a field development plan. As a result, it was decided to take part in the 2011 acquisition of the MC3D-HVG2011 PGS Multi-client Geostreamer 3D survey, covering the PL435 acreage. This dataset forms the basis for the latest generation of reservoir models. The exploration well 6507/7-15 S reached its designated total

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depth on 3 April 2012. The 6507/7-15 S well became the second gas discovery in the license, proving gas also in the West segment (Figure 2). The Jurassic Garn and Ile Formations of the Fangst Group are the main reservoir on the Zidane Field.

Following the initial discovery on Zidane, RWE Dea Norge and it’s partners have been firm on a strategy of bringing the Zidane Field forward as the first RWE Dea Norge operated field development in the company’s 40 year long history on the NCS. However, the path toward an accepted PDO application and eventually first production is demanding and requires undertaking a significant project workload. The successful fulfillment of such a diverse project relies on several factors, where the establishment of a competent internal project organization is one of the key challenges. RWE Dea Norge has managed this challenge and the current in-house Zidane field development team consists of more than 60 persons covering all relevant disciplines; Subsurface, Drilling and Completion, HSE, Procurement, Commercial, Subsea (SPS and SURF), Facilities (top side) and Operation. In addition, some 300 persons are assigned to the Zidane Field development through the various external projects currently being ran.

Managing project quality during this organic growth processes has been essential considering the fact that this is the first RWE Dea Norge operated Field Development.

In a highly competitive market, RWE has succeeded in securing the right resources for the specified tasks. Despite being inexperienced as an operator, targeted recruitment has secured highly skilled and experienced personnel that enables the project organization to perform according to plan and standards. After having successfully passed the “Beslutning om Konkretisering” (BOK) and “Beslutning om Videreføring» (BOV) decision gates, the Zidane project is now moving toward a “Plan for Utbygging og Drift” (PUD) submission by year-end 2013. The selected concept is a subsea tie-back to the existing Heidrun platform and gas export solution through the future Polarled pipeline.

The presentation for the 2013 Production Geoscience conference will aim at highlighting some of the main focus areas for the Zidane field development project, with special focus on the subsurface. Solutions to these challenges depend on the organizations ability to ensure knowledge transfer and utilize the skills and experience of the project team to its full potential. Important general project challenges will also be presented and discussed along with the approaches used to resolve them.

Figure 2 Zidane static model (Sw property) show­ ing the position of the RWE exploration wells on the East (6507/7-14 S) and West segments (6507/7-15 S), respectively­.

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Norwegian Continental Shelf (NCS) – Learning from History Kalmar Ildstad Norwegian Petroleum Directorate

In the presentation it will be given a short history and status on the NCS both with respect to areas, resources, production and technology.

Use of experience data has been a key factor for prudent resource management and thereby for the value creation we have experienced on the NCS. Learning from history has been a leading principle in the Norwegian national petroleum policy from day one. Analysis of measured data has been crucial for the technology development which has been necessary to meet new challenges. This has been a 40 year continuous development on the NCS .But it is still as important today as it has been in the past.

In the presentation the emphasis will be on how the authorities and especially how NPD are working to contribute to this development. Key words in that respect are data management, where NPD have the responsibility of making information and data from the NCS available, including collecting, quality assuring, release of nonconfidential data, administration of national core storage, establishment and participation in collaboration arenas, use of data as basis for analysis, trends and NPD -evaluations, challenge industry with respect to use of best practice and experience data etc.

At the end of the presentation some main challenges for further development of the NCS will be lined out.

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Deployment of an uncertainty analysis workflow to support concept selection – case study Sanjoy Kumar Khataniar Schlumberger Information Solutions

Uncertainty analysis is becoming a mandatory requirement for decision gate processes followed by most companies executing major investment projects. This is a case study describing how an operator, who had been using deterministic methods; was able to successfully adopt a probabilistic approach to concept selection considering sub-surface uncertainty. The study was conducted as a collaborative project between the operator and software services provider with a primary goal of creating and transferring knowledge to the operator for use in subsequent sub-surface uncertainty studies.

The field development opportunity consisted of two small, independent and possibly marginal reservoirs located close to an offshore producing field in water depths of 600-700m. The developed field produces to a sub-sea manifold and into a FPSO. The main concept considered was a tie-back to the existing manifold.

The number, type and placement of wells, producers and injectors, were a major decision variable that was of interest to stakeholders. Flow assurance was a major technical risk that needed to be incorporated. The overriding constraint was on the economics of what turned out to be a marginal field development plan.

Two exploration wells, one each in each reservoir, and a side track appraisal are the main sources of information.

Fluid samples, some core and a formation transient pressure test is available. 3D seismic is available over much of the two reservoirs and the quality is good enough to identify shale capped channel fills. Information from analogs in the proximity was available. Base 3D reservoir models of the two reservoirs had been built earlier for volumetric calculations and reservoir simulation to generate production forecasts. The reservoir models were integrated to a flow line network model to estimate production at the manifold. The existing well placements were based on the geology of the base model.

The study was initiated with a framing workshop that clarified the key stakeholders, the business decision and

the key decision criteria. The workshop reviewed all the available data and uncertainties that could potentially impact the business decision. Major uncertainties were identified and reviewed with the stakeholders. The decision variables and the level of precision with regards to computation were agreed upon early in the project.

The project team was identified and consisted of a parallel operator and service provider personnel that mirrored each other’s domains. The roles and responsibilities of the team members were clearly defined. The operator was solely responsible for taking engineering decisions while the service provider offered technology consulting.

A review of uncertainty analysis methodology was conducted and potential methodology and technologies were screened upfront. This screening considered several factors including the project deliverables, deadlines and resource expertise. An important criterion was to ensure that the methodology applied would be transparent and easy to understand by the decision makers without undermining the applicability and rigor of the analysis.

The project was managed by the operator and a joint governance board was established to deal with conflicts and external demands. The framing workshop clearly defined the project phases and deliverables at the end of each phase.

The major uncertainties were broken down into finer detail and related to model parameters as available in the base or reference model. Wherever defined model parameters were absent it was necessary to update the modeling process. Uncertainties and parameter dependencies were analyzed and recorded with a view to updating as the study progressed. Integrated static-dynamic modeling spontaneously became a key requirement. The information arising out of this analysis of uncertainties was stored in a database/ spreadsheet called the ‘uncertainty table’. Static and dynamic modeling teams independently worked and owned respective uncertainties. However, definition of uncertainties – conceptual model, parameters and their ranges and probability distributions - were reviewed

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with the entire team. Demands on keeping the model integrated, i.e. model parameter dependency was strictly honored, were given a high priority than trying to build a very sophisticated physical model. Empirical models of physical phenomena were used judiciously. These modeling decisions were reviewed with the stake holders through the governance board before being finalized.

At the end of the first phase a reference reservoir model was constructed where key parameters that influenced both static and dynamic responses of interest were satisfactorily integrated, i.e. maintained the correct dependency and resulting model behavior. Detailed sensitivity analysis was instrumental in understanding the parameters and their relationship to decision variables.

Structural uncertainty was the major player. However, it was a major challenge to model this in a satisfactory manner. Estimated and actual information from the drilling database for adjoining areas proved useful in calibrating the model realizations. Probabilistic estimates of reservoir model volumes were conducted using Monte Carlo simulation. Parameter dependencies were honored though joint probability distributions such that nonphysical and unrealistic combinations of parameter values were avoided. The total probabilistic resource for the two reservoirs was estimated by probabilistic aggregation considering complete dependency of the two reservoirs.

The approach taken to combine dynamic uncertainties was the sequential approach that has been documented extensively in the literature. This approach requires the selection of representative geological models that when combined with dynamic uncertainties and subjected to Monte Carlo simulation could satisfactorily replace a full probabilistic production forecast. Screening of geological models was performed using model responses and composite parameters generated by streamline simulation.

The range of uncertainty in resource volume, which was a major contributor to variation in production performance, was retained in the selection of geological model; however this selection was augmented by using other dynamic responses produced by streamline simulation. Selecting geological models on the basis of volumes alone could never guarantee that the ultimate production performance would retain the same relationship, i.e. a high volume case produces a high production case. It was accepted that geological model selection would be an iterative process depending on which decision or design variable was of interest further down in project time.

The streamline simulation also produced statistical information on connectivity that could be mapped across the reservoir. The connectivity and volume information were used to conceive well placement and recovery plans.

The most likely geological model was used to further refine the well types and targets. A list of possible well scenarios

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were created and evaluated to choose the most optimal combination. This final well placement configuration was used in the dynamic uncertainty analysis.

The dynamic reservoir models were built using the selected geological models and were subjected to sensitivity analysis to get more insight into model behavior and the impact of individual uncertainties. As before Monte Carlo simulation was performed using the dynamic uncertainties on each of the selected geological models. A range of recovery and production profiles were achieved that should represent all the sub-surface uncertainties. It was known that the production profile generated is dependent on the well type and placement. A rigorous approach would have been to perform a big loop optimization under uncertainty exercise. However, this was completely outside the budget available to this study.

The approach taken in this study was acceptable to the stake holders. The base well concept was modeled using the optimistic and pessimistic model scenarios to ensure it was robust and could mitigate risks as well as have the capacity to consume opportunities.

The final phase of this study was concerned with validating the selected concept with respect to flow assurance. A similar step used for screening geological models was applied to select a few representative cases from the dynamic simulations. The selection of three models was in part driven by the demand from the economics and financial department to produce a most likely and high and low scenarios. Drilling trajectories were optimized to reach the target locations and the wellbore models were finalized. Finally, the well bore models were hooked up to a flow line network model to estimate production at the manifold from the two reservoirs. The same models were used to study flow assurance issues. Economic calculations were performed on forecasted production scenarios. It was observed that the sea-bed topography and the drilling location were key parameters in the optimization of the concept. Thus, the drilling location and well trajectory model, the flow line model and flow assurance model are tightly linked and need to be studied in an integrated loop. In this study a few manual iterations were performed although a more automated approach could have been taken.

The final outcome of the study indeed proved that the reservoir was marginal and further investment would be required to reduce uncertainty and the level of risk.

However, this is in itself is a major problem with marginal reservoirs, i.e. supporting an extensive appraisal phase is not feasible. The well plan and drilling schedule has to be used in the most creative manner to produce feasible solutions. Opportunities from re-processing the seismic and reducing the uncertainty in structural interpretation is another option that needed to be evaluated.

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Reservoir Modelling, - Some key Challenges and Technical Solutions Tone Kr책kenes Software Product Manager, Roxar Software Solutions

Innovative 3D geological modeling and reservoir simulation software has proven to be effective reservoir management tools for oil and gas companies worldwide.

Based on the output from the modeling and simulation work, high level strategies for oil and gas fields can be developed, such as developing and optimizing the drainage plan for the field. Increasingly, as confidence has been gained on the reservoir models, more weight is also put on the use of the models for day-to-day operational decisions on the field.

The evolution to where we are today marks a fascinating journey where corporation between commercial oil companies, research institutions and commercial software vendors has played a crucial role. This presentation will look back at the milestones

and events that have brought 3D geological modeling forward. A key realization early on in this journey was that models will evolve as more data becomes available, and that the understanding of the sub-surface will increase as the fields mature. Hence, it became imperative to ensure repeatability of modeling workflows as more and more data becomes available, and that infrastructure for understanding model uncertainties has to be appropriately dealt with. Such uncertainties translate into operational risks, and this has to be taken into consideration when making reservoir management decisions.

Revolving the workflow of a typical asset team around the 3D reservoir models provides for a unique collaboration environment for the asset team members, who have all contributed towards the perfecting the

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understanding of the sub-surface, where only sparse data is available. All the available knowledge about the reservoir will be encompassed in the reservoir model, and through the ease of visualization of the models, it becomes natural that uncertainties and risks can be better understood, and hence decisions can be optimized by encouraging crossdomain discussions based on the reservoir models. The issue of lateral integration between domains becomes exceedingly important for oil companies, both in order to speed up reservoir management decisions, and for making the right decisions.

Significant efforts have been put into technology development that removes barriers between traditional domains, in order to address the cross-silo workflows.

Such an area of investment is that of bridging traditional seismic interpretation with that of geological modeling, whilst focusing on risk understanding. There is an opportunity for significantly speeding up the work process going from seismic interpretation to 3D geological modeling, whilst also giving the asset team efficient infrastructure and technology for addressing uncertainty trough the entire 3D modeling workflow.

A key technology breakthrough in the understanding of risk is the automation of a total uncertainty workflow, linking all the uncertainties in the static description of the reservoir to the dynamic behavior of the wells and the fluids in the reservoir. This presentation will predict some significant paradigm shifts to this effect, both in reservoir description and in the area of history matching.

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Goliat Field, a step by step improvement of the ­seismic image Johan Leutscher Eni Norge

Introduction The Goliat field is situated offshore Norway in the Barents Sea. The current holders of the Licence (PL229 and PL229B) are Eni Norge (65%) and Statoil Petroleum (35%). After the first discovery “wildcat”, drilled in 2000, the field has been investigated during the exploration phase with 4 successive wells and a side track. Development drilling started in October 2012.

Production is expected to start during fourth quarter 2014 and continue for at least 15 years. The PDO was approved in 2009 and a floating production, storage and offloading (FPSO) vessel is currently being built. There are two separate main sandstone reservoirs—Kobbe Formation and Realgrunnen Subgroup—both containing oil with an overlying gas cap. The Realgrunnen reservoir is approximately 1000 meters below sea level—equivalent to about 1.0 s two way time (twt)—and the Kobbe reservoir is at approximately 1800 meters (1.6 s twt).

Minor discoveries were made in the Snadd and Klappmyss Formations.

the summer of 2009 and covers an area of ca. 207 km2 (Figure 1).

The MAZ survey was initially processed with an anisotropic PSTM workflow and followed by an anisotropic PSDM workflow during 2010. The new survey is also planned to be the baseline survey for future application of 4D analysis. For this reason also a “phantom undershoot” at the planned FPSO location was performed.

Seismic History The licence was awarded in 1997 within the Barents Sea Round. A conventional, single azimuth, 3D seismic survey (NA9801) was acquired over the crestal area (168 km2) of the large Goliat anticline during the summer of 1998. After the discovery well 7122/7-1 in 2000 (oil in the Realgrunnen Main segment) the 3D was extended (202 km2) towards North and South in 2001 (NA0102) with the same acquisition parameters as the NA9801 survey. Both surveys were merged together into the NA01M1 survey (368 km2). In 2005 the NA01M1 survey was reprocessed. However, the quality of the existing seismic was considered not good enough for development of the field.

After a thorough feasibility study it was decided to acquire a new survey with towed streamer “MultiAzimuth” (MAZ) geometry in three different azimuth directions: azimuth 7°, azimuth 67° and azimuth 127° (azimuth 127 is the same as the 1998/2001 survey). The Q-marine MAZ survey (EN0901) was acquired during

Figure 1. Goliat – Base map showing 3D seismic surveys and exploration wells.

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Why the reshoot?

The 1998/2001 survey exhibited evidence of several imaging problems: 1) The presence of a dimming area over a certain part of the field, possibly caused by gas leakage. This makes it difficult to identify and position faults.

2) The resolution within both reservoirs was not good enough to see the expected sedimentary features like channels.

3) The presence of a complex set of faults with different orientation that makes the field highly compartmentalized with different hydrocarbon contacts, as proven by the exploration wells.

4) The presence of multiples related to the hard water bottom (although reduced after the 2005 reprocessing).

5) AVO and inversion studies didn’t give satisfactory results.

MAZ seismic acquisition In contrast to previous acquisition, a single source shooting method and a very “dense” streamer configuration (50 meters apart) were adopted in order to maximize the fold of coverage at shallow times. In addition a stronger source (3147 cu. in.) and longer cables (10 x 4000m) were used. The source and streamer array were both steerable. All three azimuths were successfully acquired within the available time window from June to mid-August and the phantom undershoot, in view of the future 4D use, was acquired along a 67° azimuth by midSeptember 2009.

Anisotropic PSTM and PSDM processing The survey acquisition was immediately followed in August 2009 by an anisotropic PSTM processing workflow resulting in 16 final TWT cubes completed in early 2010. For all 3 azimuth directions there was a near, mid, far and full angle cube and in addition there were the near, mid, far and full angle cubes of the Weighted Stack of all 3 azimuths.

The anisotropic PSTM workflow was followed by an anisotropic PSDM workflow including MAZ tomography resulting in 16 final depth cubes completed in October 2010.

Seismic interpretation Seismic interpretation started on the full angle PSTM time cube of azimuth 007, which was processed first. The interpreted TWT-surfaces were depth converted with a layer cake model using stacking velocities calibrated to the available exploration wells. Both top and base of both reservoirs could be interpreted with good confidence. The depth surfaces and fault polygons were used as input into the “PSTM geo model”.

Figure 2. Comparison between 1998 PSTM data (top) versus MAZ PSTM data (bottom).

Seismic interpretation was followed on the full angle Weighted Stack of the PSDM cubes. Also here the top and base of both reservoirs could be interpreted with good confidence. In addition the fault planes could be interpreted directly in the depth domain. The well calibrated surfaces and fault planes were directly used as input into the “PSDM geo model”.

A wide range of seismic attributes was tested on the cubes to enhance the identification of fault planes and sedimentary features.

Results For the reservoir depth prognosis of the development wells three different depth maps were available: PDO, PSTM and PSDM depth maps. In addition P10 and P90 maps (of 500 maps) were used from the uncertainty study work related to the PDO. Main uncertainty was related to the flanks of the structure. The results of the first development wells showed that the PSDM depth maps were the most accurate, followed by the PSTM and the

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PDO maps.

The comparison between the newly acquired azimuth 127째 and vintage data (1998-2001) shows a step change improvement in term of spatial /vertical resolution, event continuity and Signal-to-Noise ratio. Channels within the Realgrunnen reservoir are now clearly visible. Comparison between azimuth 127째 only and Weighted Stack from all three azimuths indicate that the MAZ survey has been successful in delivering improved illumination and S/N. Faults are more clearly imaged, shadow zones are greatly reduced and better continuity is observed at several levels.

A comparison between vintage PSTM data and new anisotropic MAZ PSTM data is shown in Figure 2. The depth imaging approach, using anisotropic multi-azimuth tomography, further step improved the imaging of both targets (Figure 3) resulting in a reduction of the structural uncertainty.

Figure 3. Comparison between MAZ 2009 PSTM (left) and PSDM (right).

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Oseberg Field – A ground-breaking history Helge Rutledal, Kim Hatland Statoil

The giant Oseberg Brent Field is located offshore Norway 140 km Northwest of Bergen at approximately 100 m water depth. The field is developed from the Oseberg Field Centre (OFC) and the Oseberg C (OSC) platforms - both installations with process and drilling facilities. Production started in 1988 from the OFC and in 1991 from the OSC. The Oseberg Field Center has been and still is an important hub for satellite tie-in and other producing field in the area.

The Oseberg Field was discovered in 1979 by well 30/6-1 drilled in the gas cap of the accumulation. During the next few years 7 additional appraisal wells were drilled in order to delineate the field before the phase 1 Field Development Plan (FDP) was finalized and approved in 1983. Later on an additional 4 wells were drilled for the second phase FDP which in addition to the northern part of the field (OSC) also included an update of phase 1 FDP.

The Oseberg Field produces from different formations and segments (Alpha Main, Alpha North, Alpha South, Gamma Main and Gamma South). The segments are separated by faults with varying throw leading to a fairly complex flow pattern during production. The producing formations belong to the Middle Jurassic Brent Group and are subdivided (from below and upwards) into the Oseberg, Rannoch, Etive, Ness and Tarbert formations of which the Rannoch formation is considered non reservoir.

While the recovery factors for the more homogeneous Oseberg, Etive and Tarbert Fm. are in top world class it is a continuous challenge to increase reserves for the fluvial reservoir systems in Ness Fm. Not only is it difficult to map the individual channel systems but also the drilling of Ness sands is quite challenging due to instabilities of the shale, coal and paleosol present between the channel sand systems. It can be argued if it has been a wise strategy to postpone the Ness well drilling late in the lifetime of the field.

Figure 1. Location of the Oseberg Main Field (left) and a 3 D map showing the different structural elements of the field (right).

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The field is by September 2013 producing approximately 75 000 bbl/d. Plateau production of 500 000 bbl/d ended in 1997. About 92 percent of the expected reserves have now been produced and the original oil leg of 215 m is reduced to somewhere between 10 to 20 m in the main reservoirs. So the field is definitively on tail production. However, the Oseberg area is still an attractive area with a liquid reserve replacement ratio higher than 1.0 every year since 2007. The expected reserves (Brent Gp. only) estimate is 398 MSm³ resulting in a record high ultimate recovery factor of 70%.

The choice of drainage strategy with gas injection as the main drive mechanism has proved to be very successful.

The production drive mechanism for the Oseberg Field is currently reinjection of associated gas. With oil and water being produced in addition, reinjection of gas is not (and has not been) sufficient to maintain reservoir pressure.

Hence, between 1993 and 2001 approximately 21 GSm³ of gas was imported from the nearby Troll Field and injected to better maintain the reservoir pressure. The gas blow down, defined as the cease of all gas injection, was initially planned to start in 2010. Due to a continuous increased understanding of the field and an accompanying increased oil reserve and resource basis, the gas blow down has been postponed several times. Today the gas blow down is planned to start in 2020. This could, however, still be subject to re-evaluation.

With massive reinjection of produced gas, the gas treatment capacity has been the main bottleneck since oil production declined in 1997. Production is optimized on a daily basis based on individual well gas oil ratio.

The gas treatment capacity has been upgraded several times, both at the OFC and OSC, and today the total gas handling capacity is 41 MSm³/sd. The OFC covers the southern area of the field with highest reserves and has a gas handling capacity of 32 MSm³/sd. The capacity at the Oseberg C is still not dimensioned adequately according to in-place volumes, and unprocessed well stream is therefore sent to the OFC in a pipeline (MTS – Multiphase Transportation System) to balance the processing capacity in a more optimal way.

The Oseberg Field has been an offshore laboratory to implement new technologies as they have emerged during the late eighties, the nineties and into the new century and still is. The presentation will discuss both successful and less successful technologies and projects which have been implemented.

Nearly all of the production is currently coming from horizontal wells that have been drilled in the field since the early nineties. Typical horizontal well lengths are between 1200 m to 3000 m and are placed fairly close to the water oil contact. Extensive and accurate geo-steering by use of navigator and autotrack has been applied for optimum well placement. Multilateral wells with both branches exceeding 1200 m have been successfully established.

To increase the recovery and accelerate tail end production, completions with zone control in the horizontal wells are assumed to play an important role.

Currently several wells, both single branched and multi branched, have been completed with zone control.

Figure 2. The graph shows the development of the estimated total Oseberg Main Field reserves as they have developed through time from the FDP in 1983 until 2013.

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Even though, wire line, coiled tubing and well tractors have been applied at a fairly large scale in order to monitor the gas flooding of the reservoir and well inflow profiles, It has been recognized that data acquisition and value of “early” information typically are underestimated and have not been given high enough prioritization. PLT’s in the long horizontal reservoir sections always tend to give unexpected results. These data have been of vital importance to the design of interventions to further optimize production from the wells.

Even with hundreds of well penetrations in the Brent Gp. reservoirs through the life time of the field, it has become evident as new challenges appear that the data coverage is still too sparse for some data types and stratigraphic levels. An updated data acquisition strategy is made as compensating measure.

Oseberg has been in the front-line to utilize geophysical technology and shot a full field 3D seismic already in 1982. 4D seismic was acquired in the central area of the field in 1989 as one of the first world 4D pilots.

In 1998 part of the field was covered with seabed seismic and a new conventional 3D seismic to map out remaining reserves was shot in 1992. Later on, four monitoring 4D seismic surveys have been acquired.

The most important IOR measure for the Oseberg Field has been infill drilling. Except for a short stop in drilling from the OSC platform, the OSB and OSC drilling facilities have been used continuously until the recent major drilling upgrade stops. Current drilling schedules ends in 2024 and 2018 for the OSB and OSC platforms, respectively. Even in the homogeneous Oseberg Fm. the infill potential is high – much higher than the history matched reservoir simulation models are able to predict. One important lesson learned from this field is to dimension for future infill potential. This affect choice of drilling rig, number of well slots and casing programme in wells that could be sidetracked at a later stage. To build in flexibility on the platforms regarding free space for future installations and extra J-tubes for future possible tie-ins have also proven to be quite successful.

Figure 3. The sub-surface team on the Oseberg Field has been awarded the ­Norwegian Petroleum Directorates’s prize for IOR 2012 for world class ­performance in increased recovery.

Last year the Oseberg Field sub-surface team was awarded NPD’s IOR prize. The following comment was given from NPD: “The work, particularly regarding reservoir aspects are characterized by a long-term mindset, not a desire for short-term rewards”. This is a tribute to all the good sub-surface work performed in the past. We are currently looking ahead and even though the Oseberg Main Field now is far out on the tail end when it comes to oil production, the remaining reserves and resources are considerable and new ideas and brave initiatives will most likely pay off when they are realized.

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Lessons learned from existing mature fields GULLFAKS: NEVER SAY NEVER! Rolf Smedal Gullfaks Bergen

The Gullfaks field is located at the heart of the Tampen area. This is a world class petroleum region, and the field has been at the forefront of technology development on NCS. This has allowed a huge reserve growth, and remaining opportunities are numerous. In line with the technological development, there has been a lot of experience gained during the 27 years of Gullfaks history!

Reservoir drainage: All available platform slots are in use, but the relative number of injectors vs. producers has changed based on production performance. Both within segments and also across segment boundaries the lateral communication appeared to be better than our pre-production assumptions. Consequently compared to the PDO fewer injection points were needed, and the injectors have also been drilled deeper in the water basin and thus further away from their related producers. The benefit of this has been reduced cost, more producers, improved sweep and less associated H2S.

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Good tie-in flexibility was installed as a part of the Gullfaks platforms, and this visionary design today allows for 11 (13) subsea tie-ins to GFA and GFC. Time-lapse seismic was invented during the early Gullfaks days, and since then seismic monitoring has been an absolute premise for the successful story of the field. Our history tells the importance of including 4D as an overall strategy as early as possible and then follow this consistently through life of field. And note; geophysical monitoring can appear to have additional benefits not foreseen at start up.

A Gullfaks challenge shared with many fields is how to produce from adjacent reservoirs with contrasting properties? A groundbreaking approach including fracturing has proved to be cheap and successful.

Overburden: It has appeared that simple inherited assumptions on stratigraphy and rock properties in the overburden needed revision. This has been due to HSE issues such as well integrity, injection parameters, drillability etc. These topics have involved all Petec disciplines, and 4D, geoand sim modeling, rock mechanics and other traditionally reservoir technologies have successively been applied in shallower strata.

Gullfaks has a very successful CRI (Cuttings Reinjection) history based on diverge injection in many wells. Still – based on recent experiences gained by the industry – also the CRI operation, planning and monitoring have gained increased focus from Petech the recent years.

Similarly renewed focus has been related to the Gullfaks test production from the Shetland Lista interval which is stratigraphically above our traditional reservoirs.

Experience tells us that an open mindset should be kept for both reservoir and overburden. Accepted truths always need to be challenged, and applying traditional Petech approaches in alternative settings can be both important and fun!

Others When facing new challenges even with hundreds of Gullfaks wells it becomes evident that for some data types and stratigraphic levels the data coverage is too sparse.

Experience tells us that cost and unrealized values due to lack of data far exceeds unnecessary acquisitions - if any!

Field infrastructure and surface installations are aspects typically hardly considered by G+Gs. Still, such subjects have shown to implicate subsurface activities in ways not foreseen initially. If imagine Gullfaks development

starting today it is likely that some of the infrastructure architecture elements should have been challenged during PDO. This experience should be kept in mind for future field developments.

When transferring from a PDO phase to production an overwhelming amount of data will approach! Before startup it is advised to prepare a hyperstrict plan for dataflow and storage! And as we have experienced numerous times; there might be a lot of valuable information in old and typically neglected data.

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Geological knowledge transfer in production ­workflows – new opportunities Lars Sønneland Schlumberger Stavanger Research

Generation of a Model Repository – Seismic DNA In our reservoir characterization methodology the concept of generating a Model Repository is important.

The Model Repository is defined as the set of geometric primitives that might be extracted from 3D seismic data such as horizons and faults and will as such represent the basis for any type of model-building. One technology that is used for generating such primitives is Seismic DNA .The method is able to do non-local searches on multiattribute data-sets. First the input data is transformed into characters. The input data can be seismic data, seismic attributes or any other data types. The transformed data is searched for complex features given by charactersignatures using text-based search technology. The method presented allows for feature patterns with varying lengths, varying attribute values and a varying number of subfeatures in the character-signature. To make the design of the character-signatures user-friendly a fully visually guided process is implemented that enables the user to compose the character-signatures without knowledge of the underlying technology. Since this search methodology for character-signatures holds many similarities with DNA searches for human genes it is named Seismic DNA Search. One example on how the method works is given in figure 1. The user defines the sequence of interest by

using the interactive Seismic DNA framework (Bakke et al., 2012). A translator converts the numerical input data from the model space into text strings in the DNA space, red, green and blue colors on the right side in Figure 1. On the left side in this figure, the data is displayed in model space.

The corresponding search expression, a gene, is defined through a regular expression. A visual description of the gene is shown on the right side in Figure 1. The colors in the gene correspond to the colors of the translated inline.

The first and the second column in the gene represent the minimum and the maximum length respectively of the current color of the gene. This search is performed on all traces in the 3D volume, and the output is three point strings representing hits of the three respective events.. To detect this change in the “seismic reflection texture” , the seismic DNA needs to be modified. In Figure 2 the DNA change is highlighted. The seismic data is now translated into four colors, yellow, green, red and turquoise. A visual description of the modified gene is shown on the right side in Figure 2. The modified DNA is subsequently applied to search along the traces that did not get any hits during the previous iteration. There is a significant amount of hits on both sides of the fault in Figure 2. These hits will improve

Figure 1. The search result of the first iteration and the corresponding gene. The seismic data is translated into three colors, red, green and blue in the DNA space.

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Figure 2. The search result of the first and the second iteration and the modified gene from the second iteration on the right hand side. The seismic data is translated into four colors, yellow, green, red and turquoise in the DNA space.

the surface extraction across the fault(s) by relating the sequence of events correctly. All hits from the second iteration are added to the hits from the first iteration as a collection of point sets. They may serve as seed points to the surface sequence extraction. The DNA method is flexible in terms of number of iterations. This number is dependent of the lateral variability of the seismic texture.

Generation of a Model Repository – Faults and ­fractures The other important primitives in the Model Repository are the faults set or more generally discontinuities. Subtle fault extraction is potentially provided by AntTracting , but our focus will be on large fault extraction.

In the current state-of-the-art, large listric faults are not fully extracted as complete 3D surface objects, but rather as a set of small patches that require extra-editing from the interpreter.

The proposed methodology is highlighted in figure 3 .

3D geometrical properties such as azimuth and planarity are calculated for each non-zero voxel in the 3D binary cube using principal component analysis and neighborhood information. A set of rules is thus built based on these new attributes leading to a sequence of new constrained binary cubes with a value of 1 for the discontinuity, and 0 elsewhere.

Figure 3. Example of Automatic fault extraction workflow

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Figure 4. Example of large faults. Left: Seismic section showing multi-scale faults. Right: Final edge cube overlaid on seismic.

The individual point sets representing the faults are finally best-fitted with 3D mesh triangulations which result in fault surfaces or patches. These faults can be filtered by size, orientation, and ready to use in further interpretation, geological or geomechanical modelling.

Some examples of this application are highlighted in figures .

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Notes:

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Production Geoscience 2013 Can experiences from mature fields help “New Field development�?