Technology Focus: History Matching and Forecasting (April 2011)
- Marco A. Cardoso (Petrobras Research Center)
- Document ID
- Society of Petroleum Engineers
- Journal of Petroleum Technology
- Publication Date
- April 2011
- Document Type
- Journal Paper
- 96 - 96
- 2011. Copyright is retained by the author. This document is distributed by SPE with the permission of the author. Contact the author for permission to use material from this document.
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History matching and forecasting of geologically complex reservoirs are challenging aspects for efficient reservoir management. The foremost reason is the high level of uncertainty that exists in the reservoir models because of the limited, sparse, and multiscaled reservoir data available. The problem is further heightened for deepwater provinces such as the Gulf of Mexico, Brazil, and West Africa.
The objective of the history matching is to adjust the geological model and its parameters (e.g., permeabilities and porosities) such that the simulation model is able to reproduce the well-flow-rate and pressure histories reasonably. The result of the history-matching process is a new simulation model that can be very different from the original geological model. However, this history matching is an inverse problem, meaning that there is no unique solution. Different arrangements of reservoir parameters can furnish many simulation models tuned to the available past data. Even though each matched reservoir model is capable of reproducing the observed data, these various geological models can generate different production forecasts. The greatest challenge is obtaining multiple efficient history-matched models for realistic uncertainty estimation.
Conventional history matching is a trial-and-error process. The mismatch between observed and simulated values is minimized by adjusting reservoir parameters over successive simulation runs. Even for experienced reservoir engineers, the process is very time consuming and, in general, a single adjusted scenario can be geologically inconsistent. On the other hand, modern history-matching techniques apply numerical optimization and generate multiple geologically consistent adjusted scenarios.
Most of the history-matching research work being carried out today can be classified into four types of algorithms: gradient-based methods, stochastic methods, data-assimilation approaches, and streamline techniques. Data-assimilation techniques, such as an ensemble Kalman filter, represent a very active research area, and new and potentially promising methodologies have been developed. The papers summarized in this feature and those in the additional-reading list provide good examples of new methodologies.
History Matching and Forecasting additional reading available at OnePetro: www.onepetro.org
SPE 129183 “A New Adaptively Scaled Production-Data-Integration Approach Using the Discrete Cosine Parameterization” by Eric Bhark, SPE, Texas A&M University, et al.
SPE 131627 “History Matching With Sensitivity-Based Parameter Modifications at Gridblock Level” by H. Almuallim, SPE, FirmSoft Technologies, et al.
SPE 141216 “History Matching a Field Case Using the Ensemble Kalman Filter With Covariance Localization” by Alexandre A. Emerick, SPE, Petrobras, et al.
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