Rapid Construction of Ensembles of High-resolution Reservoir Models Constrained to Production Data

Author(s):  
C. Scheidt ◽  
J. Caers ◽  
Y. Chen ◽  
L. Durlofsky
1998 ◽  
Vol 50 (12) ◽  
pp. 72-76 ◽  
Author(s):  
Akhil Datta-Gupta ◽  
Seongsik Yoon ◽  
Indranil Barman ◽  
D.W. Vasco

SPE Journal ◽  
1998 ◽  
Vol 3 (04) ◽  
pp. 344-355 ◽  
Author(s):  
Xian-Huan Wen ◽  
C.V. Deutsch ◽  
A.S. Cullick

2001 ◽  
Vol 7 (S) ◽  
pp. S65-S73 ◽  
Author(s):  
Dean S. Oliver ◽  
Albert C. Reynolds ◽  
Zhuoxin Bi ◽  
Yafes Abacioglu

SPE Journal ◽  
2006 ◽  
Vol 11 (04) ◽  
pp. 464-479 ◽  
Author(s):  
B. Todd Hoffman ◽  
Jef K. Caers ◽  
Xian-Huan Wen ◽  
Sebastien B. Strebelle

Summary This paper presents an innovative methodology to integrate prior geologic information, well-log data, seismic data, and production data into a consistent 3D reservoir model. Furthermore, the method is applied to a real channel reservoir from the African coast. The methodology relies on the probability-perturbation method (PPM). Perturbing probabilities rather than actual petrophysical properties guarantees that the conceptual geologic model is maintained and that any history-matching-related artifacts are avoided. Creating reservoir models that match all types of data are likely to have more prediction power than methods in which some data are not honored. The first part of the paper reviews the details of the PPM, and the next part of this paper describes the additional work that is required to history-match real reservoirs using this method. Then, a geological description of the reservoir case study is provided, and the procedure to build 3D reservoir models that are only conditioned to the static data is covered. Because of the character of the field, the channels are modeled with a multiple-point geostatistical method. The channel locations are perturbed in a manner such that the oil, water, and gas rates from the reservoir more accurately match the rates observed in the field. Two different geologic scenarios are used, and multiple history-matched models are generated for each scenario. The reservoir has been producing for approximately 5 years, but the models are matched only to the first 3 years of production. Afterward, to check predictive power, the matched models are run for the last 1½ years, and the results compare favorably with the field data. Introduction Reservoir models are constructed to better understand reservoir behavior and to better predict reservoir response. Economic decisions are often based on the predictions from reservoir models; therefore, such predictions need to be as accurate as possible. To achieve this goal, the reservoir model should honor all sources of data, including well-log, seismic, geologic information, and dynamic (production rate and pressure) data. Incorporating dynamic data into the reservoir model is generally known as history matching. History matching is difficult because it poses a nonlinear inverse problem in the sense that the relationship between the reservoir model parameters and the dynamic data is highly nonlinear and multiple solutions are avail- able. Therefore, history matching is often done with a trial-and-error method. In real-world applications of history matching, reservoir engineers manually modify an initial model provided by geoscientists until the production data are matched. The initial model is built based on geological and seismic data. While attempts are usually made to honor these other data as much as possible, often the history-matched models are unrealistic from a geological (and geophysical) point of view. For example, permeability is often altered to increase or decrease flow in areas where a mismatch is observed; however, the permeability alterations usually come in the form of box-shaped or pipe-shaped geometries centered around wells or between wells and tend to be devoid of any geologica. considerations. The primary focus lies in obtaining a history match.


2020 ◽  
Vol 496 (1) ◽  
pp. 199-207 ◽  
Author(s):  
Tor Anders Knai ◽  
Guillaume Lescoffit

AbstractFaults are known to affect the way that fluids can flow in clastic oil and gas reservoirs. Fault barriers either stop fluids from passing across or they restrict and direct the fluid flow, creating static or dynamic reservoir compartments. Representing the effect of these barriers in reservoir models is key to establishing optimal plans for reservoir drainage, field development and production.Fault property modelling is challenging, however, as observations of faults in nature show a rapid and unpredictable variation in fault rock content and architecture. Fault representation in reservoir models will necessarily be a simplification, and it is important that the uncertainty ranges are captured in the input parameters. History matching also requires flexibility in order to handle a wide variety of data and observations.The Juxtaposition Table Method is a new technique that efficiently handles all relevant geological and production data in fault property modelling. The method provides a common interface that is easy to relate to for all petroleum technology disciplines, and allows a close cooperation between the geologist and reservoir engineer in the process of matching the reservoir model to observed production behaviour. Consequently, the method is well suited to handling fault property modelling in the complete life cycle of oil and gas fields, starting with geological predictions and incorporating knowledge of dynamic reservoir behaviour as production data become available.


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