Integrating Geoscience and Engineering for Improved Field Management and Appraisal

Gawith, D.; Gutteridge, P.; Tang, Zepeng

doi:10.2118/29928-ms

Cited by 8 publications

(2 citation statements)

References 2 publications

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“…The Shared Earth Model concept developed in the early 1990's led to a step-change in the integration of reservoir description [Gawith, 1995, Gutteridge 1996. Workflows for upscaling, gridding and construction of geological and simulation models are getting faster and easier to use.…”

Section: Better Integrated Reservoir Description and Workflowsmentioning

confidence: 99%

Top-Down Reservoir Modelling

Williams¹,

Mansfield²,

MacDonald³

et al. 2004

Proceedings of SPE Annual Technical Conference and Exhibition

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TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractOver the last twenty or more years of reservoir performance prediction through simulation there have been only two fundamental changes. First was the evolutionary increase in computing speed that has allowed larger, more detailed reservoir models to be built. Second was the revolutionary change in approach that involved the entire subsurface community in building integrated reservoir descriptions. The next big change may in time prove to be BP's Top-Down Reservoir Modelling (TDRM). This is a new pragmatic approach to fully incorporate reservoir uncertainty in model construction and performance prediction.

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Section: Better Integrated Reservoir Description and Workflowsmentioning

confidence: 99%

Top-Down Reservoir Modelling

Williams¹,

Mansfield²,

MacDonald³

et al. 2004

Proceedings of SPE Annual Technical Conference and Exhibition

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“…The Shared Earth Model concept originated in the early 1990s when it became apparent that 3D modelling had the potential to integrate the hitherto separate views of a reservoir held by reservoir engineers and by geoscientists (1). It has since been widely taken up and developed further; and practical demonstrations of shared earth model construction and application have been presented and discussed by oil companies and software vendors (2), (3).…”

Section: Introductionmentioning

confidence: 99%

Decision-Driven Reservoir Modelling: The Next Big Thing

Gawith¹,

Gutteridge²

1999

Proceedings of SPE Reservoir Simulation Symposium

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TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractIn the past few years there has been a change in reservoir modelling from using 2D maps to a 3D workflow involving detailed shared earth models, upscaled for reservoir simulation. This change has brought an improvement in the quality of simulation models through incorporating more geological features of reservoirs. However, in many cases, this process is slow and impractical. Teams find they cannot update the complex models frequently enough, or carry uncertainty, because current technology can not cope. Our task now is to take the shared earth model forward and realise its potential. This paper introduces a new approach to reservoir modelling: DDRM, which is aimed at meeting the challenges of the new business environment. It involves creating and using reservoir models which are just as complex as they need to be, and bringing uncertainty into predictions more thoroughly than before. This requires advanced reservoir modelling technology, with new techniques such as: scaleindependent non-grid-based design, model-detail realisation on the fly, merging static and dynamic elements, and uncertain prediction from uncertain models.The new approach is illustrated by a case study in probabilistic ranking of well-locations on an oilfield under development. The business decision is where to put the next wells, and the pace of development makes it impossible to update and run conventional large reservoir models to evaluate all the possibilities. The DDRM solution is quick and appropriate, matching method to question. New technology used in this case includes model construction with uncertain objects, dynamic characterization by streamline simulation on the fly, detail created locally when required and automated, rapid model updating .

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Modern Approach to Estimation of Uncertainty of Predictions With Dynamic Reservoir Simulation—A Case Study of a German Rotliegend Gas Field

Junker

Dose

Plas

et al. 2006

SPE Annual Technical Conference and Exhibition

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A deep German onshore Rotliegendes gasfield in a complex sedimentary and structural environment is subject to evaluation of the remaining potential for additional wells and possible recovery acceleration. A graben structure filled with aeolian sediments in an overall depth of about 4900m comprises the dry gas bearing reservoir. The field is developed by a limited number of expensive wells with a relatively wide well spacing. The wells face complicated drilling conditions and a necessary deviation to honour environmental issues which severely limit the number of well site locations. The quality of the 3D seismic is limited because of Zechstein salt intrusions in the overburden. Hence the structural and interpreted fault models are subject to significant uncertainties. Characterisation of aeolian sedimentary facies distribution and quality, which suffers from diagenetic effects, is achieved by a geostatistical approach. The model is mainly based on information from cores and logs. Seismic information could only be used to guide the distribution of the aeolian reservoir facies and a probably unproductive fanglomerate facies type. One main question is the level of compartmentalisation of the field due to faults and flow barriers related to reservoir facies distribution. During the dynamic modelling process possible scenarios are checked against the observed production data regarding communication and GIIP distribution. This gives a range of possibilities leading to a significant uncertainty regarding the prediction of future production and the effect of acceleration wells. There is room for only a few more wells. The decision where to place these is supported by the integrated modelling approach. This has been chosen to address a wide range of uncertainty of both static and dynamic effects. The static effects have been addressed by a combination of geostatistical static reservoir modelling with fully 3D static uncertainty estimation. The basis for the dynamic modelling part is a family of realisations defined by a process to identify "most diverse" representative scenarios out of bigger number of geological realisations. Computer aided history matching, experimental design and proxy solutions gave a very effective approach to handle the uncertainty estimation with the dynamic reservoir simulation. The fully integrated workflow allowed an optimized process to estimate the uncertainty of prediction within a limited time frame. The main subject of this paper is the process of dynamic reservoir simulation using these techniques and discussion of the results. Introduction The main weakness associated with modeling which utilises a detailed reservoir description to reproduce observed dynamic behaviour is the ill-posed nature of the inverse problem for which a solution is sought. The non-uniqueness of solutions 1 in this field of complex inverse problems is well known in the industry 2,3,4. Sparse control data burdened with uncertainty, measurement errors and questions about representativeness in almost all cases lead to highly non-linear responses. It is seldom the case in reality that only one unique history match (HM) solution exists; typically examples where this appears to occur are either synthetic, obviously highly constrained or extremely simple 5. Traditionally this generic problem has been more or less ignored (at least in the daily business of the reservoir modeling workflow), primarily due to cost in terms oftime andhardware. In the early 1990s the concept of the Shared Earth Model 6 lead to a stepwise integration of more and more data on an increasingly detailed level. Availability of cheap computing power coupled with sophisticated software applications has lead to increased complexity in the static reservoir models, leading to the requirement to upscale to a simulation scale to perform any form of dynamic modeling at all in a practical timeframe.

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Integrating Geoscience and Engineering for Improved Field Management and Appraisal

Cited by 8 publications

References 2 publications

Top-Down Reservoir Modelling

Top-Down Reservoir Modelling

Decision-Driven Reservoir Modelling: The Next Big Thing

Modern Approach to Estimation of Uncertainty of Predictions With Dynamic Reservoir Simulation—A Case Study of a German Rotliegend Gas Field

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