Integration of production data into reservoir models

Oliver, Dean S.; Reynolds, Albert C.; Bi, Zhuoxin; Abacioglu, Yafes

doi:10.1144/petgeo.7.s.s65

Cited by 66 publications

(36 citation statements)

References 56 publications

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“…The optimisation scheme that we have chosen is a variant of a Genetic Algorithm, and is described in the third section . We did not concur with the view that Genetic Algorithms veere not an appropriate optimisation method for reservoir characterisation [7] . The next sections examine the effect of variations in the Genetic Algorithm structure and compares our results with other methods .…”

Section: European Conference On the Mathematica Of Oil Recove Ry -Frementioning

confidence: 60%

“…Each method has advantages and disadvantages, but no single method has met with general acceptance by the reservoir engineering community, a review of these methods can be found elsewhere [2] . It has been suggested [7] that Genetic Algorithms would not be a good choice as an optimiser for this problem . Our reasons for selecting the GA are based on four considerations : 1 .…”

Section: Generating a Reservoir Mode Lmentioning

confidence: 99%

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Using Genetic Algorithms to Invert Numerical Simulations

Carter¹,

Romero²

2002

ECMOR VIII - 8th European Conference on the Mathematics of Oil Recovery

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Pri n ce Co n sort R oad , South Ken sing ton , London , SW7 26P . Un ited Ki n gdom Email : j . n .ca rter@ic . ac. u k Abstrac tThe use of automated inversion methods to condition numerical reservoir models to both static data (we)l-logs) and dynamic data (production data) is becoming more important . It is essential that any methodology should be : robust to problems in the numerical simulation ; able to handle all of the different classes of variables present ; and be efficient both in terms of the number of simulations required and the wal)-clock time taken .In this paper we demonstrate that by using a combination of : geostatistical interpolation ; a pilotpoint method ; and a Genetic Algorithm, one can successfully match a complex Brent sequence reservoir to both production and wel)-log data . The reservoir is a version of the PUNQ Complex Model, which consists of a realistic Brent sequence, within a complex fault system . There are Bleven producers and six injectors, and a total of four years of production was used in the test . The model is characterised by using 513 points for the permeability, the porosity and the net-togross ratio . An additional group of 24 variables are used to control the geostatistical properties, the fault properties, and the wel)-skin factors, this gives a total of 1563 variables .The Genetic Algorithm used was the standard generational replacement with elitism method . The structure of the genome and the crossover operators veere specially designed for the problem being tackled . Using fust 400 numerical simulations it was possible for the methodology to obtain a match similar in quality to that which might be obtained by a good reservoir engineer . Using 20 computers the wal)-clock time taken would have been equivalent to the time taken by 20 numerical simulations . The method we have used is robust to loss of simulation results, able to handle many classes of variables simultaneously and is efficient .

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Section: European Conference On the Mathematica Of Oil Recove Ry -Frementioning

confidence: 60%

Section: Generating a Reservoir Mode Lmentioning

confidence: 99%

Using Genetic Algorithms to Invert Numerical Simulations

Carter¹,

Romero²

2002

ECMOR VIII - 8th European Conference on the Mathematics of Oil Recovery

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“…Gradient methods fall into two classes, depending on the number of derivatives to be calculated [124]. If the number is small, then direct calculation of the objective function gradient is best.…”

Section: Derivative-free Methodsmentioning

confidence: 99%

Geological Modelling and Reservoir Simulation

Farmer

2005

Mathematics in Industry

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Summary.The main mathematical techniques used in building geological models for input to fluid flow simulation are reviewed. The subject matter concerns the entire geological and reservoir simulation modelling workflow relating to the subsurface. To provide a realistic illustration of a complete fluid flow model, a short outline of two-phase incompressible flow through porous media is given. The mathematics of model building is discussed in a context of seismic acquisition, processing and interpretation, well logging and geology. Grid generation, geometric modelling and spatial statistics are covered in considerable detail. A few new results in the area of geostatistics are proved. In particular the equivalence of radial basis functions, general forms of kriging and minimum curvature methods is shown. A Bayesian formulation of uncertainty assessment is outlined. The theory of inverse problems is discussed in a general way, from both deterministic and statistical points of view. There is a brief discussion of upscaling. A case for multiscale geological modelling is made and the outstanding research problems to be solved in building multiscale models from many types of data are discussed.

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“…Moreover the number of gradzones that can be used is restricted because of the inefficient gradient simulator methods used to generate sensitivity coefficients. Although Schlumberger Evaluation & Production Services is developing improved automatic history matching software for data integration based on some of our research funded previously by DOE (Oliver et al (2001); Li et al (2003a); Reynolds (2002b, 2003)), the costs of this software will be prohibitive for many independent water saturation (Krocw) is fixed and is not treated as a model parameter when both absolute permeabilities and relative permeability are adjusted during history matching. In cases that Krocw is chosen to be a model parameter, checkbox "Krocw" needs to be checked.…”

Section: Description Of Taskmentioning

confidence: 99%

Data Integration for the Generation of High Resolution Reservoir Models

Reynolds

Oliver

et al. 2009

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The goal of this three-year project was to develop a theoretical basis and practical technology for the integration of geologic, production and time-lapse seismic data in a way that makes best use of the information for reservoir description and reservoir performance predictions. The methodology and practical tools for data integration that were developed in this research project have been incorporated into computational algorithms that are feasible for large scale reservoir simulation models. As the integration of production and seismic data require calibrating geological/geostatistical models to these data sets, the main computational tool is an automatic history matching algorithm. The following specific goals were accomplished during this research. (i) We developed algorithms for calibrating the location of the boundaries of geologic facies and the distribution of rock properties so that production and time-lapse seismic data are honored. (ii) We developed and implemented specific procedures for conditioning reservoir models to time-lapse seismic data. (iii) We developed and implemented algorithms for the characterization of measurement errors which are needed to determine the relative weights of data when conditioning reservoir models to production and time-lapse seismic data by automatic history matching. (iv) We developed and implemented algorithms for the adjustment of relative permeability curves during the history matching process. (v) We developed algorithms for production optimization which accounts for geological uncertainty within the context of closed-loop reservoir management. (vi) To ensure the research results will lead to practical public tools for independent oil companies, as part of the project we built a graphical user interface for the reservoir simulator and history matching software using Visual Basic.iii

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Integration of production data into reservoir models

Cited by 66 publications

References 56 publications

Using Genetic Algorithms to Invert Numerical Simulations

Using Genetic Algorithms to Invert Numerical Simulations

Geological Modelling and Reservoir Simulation

Data Integration for the Generation of High Resolution Reservoir Models

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