Use of Geostatistical Modeling and Automatic History Matching to Estimate Production Forecast Uncertainty - A Case Study

2002 ◽  
Author(s):  
Fletcher Bennett ◽  
Thomas Graf
Keyword(s):  
History Matching ◽  
Geostatistical Modeling ◽  
Production Forecast ◽  
Forecast Uncertainty ◽  
Automatic History Matching

Caveats and Pitfalls of Production Forecast Uncertainty Analysis Using Design of Experiments

2021 ◽  
Author(s):  
Boxiao Li ◽  
Hemant Phale ◽  
Yanfen Zhang ◽  
Timothy Tokar ◽  
Xian-Huan Wen
Keyword(s):  
Design Of Experiments ◽  
Uncertainty Analysis ◽  
History Matching ◽  
Petroleum Industry ◽  
Production Forecast ◽  
Forecast Uncertainty ◽  
Field Development ◽  
Development Alternatives ◽  
Modeling Principles ◽  
Made In

Abstract Design of Experiments (DoE) is one of the most commonly employed techniques in the petroleum industry for Assisted History Matching (AHM) and uncertainty analysis of reservoir production forecasts. Although conceptually straightforward, DoE is often misused by practitioners because many of its statistical and modeling principles are not carefully followed. Our earlier paper (Li et al. 2019) detailed the best practices in DoE-based AHM for brownfields. However, to our best knowledge, there is a lack of studies that summarize the common caveats and pitfalls in DoE-based production forecast uncertainty analysis for greenfields and history-matched brownfields. Our objective here is to summarize these caveats and pitfalls to help practitioners apply the correct principles for DoE-based production forecast uncertainty analysis. Over 60 common pitfalls in all stages of a DoE workflow are summarized. Special attention is paid to the following critical project transitions: (1) the transition from static earth modeling to dynamic reservoir simulation; (2) from AHM to production forecast; and (3) from analyzing subsurface uncertainties to analyzing field-development alternatives. Most pitfalls can be avoided by consistently following the statistical and modeling principles. Some pitfalls, however, can trap experienced engineers. For example, mistakes made in handling the three abovementioned transitions can yield strongly unreliable proxy and sensitivity analysis. For the representative examples we study, they can lead to having a proxy R2 of less than 0.2 versus larger than 0.9 if done correctly. Two improved experimental designs are created to resolve this challenge. Besides the technical pitfalls that are avoidable via robust statistical workflows, we also highlight the often more severe non-technical pitfalls that cannot be evaluated by measures like R2. Thoughts are shared on how they can be avoided, especially during project framing and the three critical transition scenarios.

Software Assisted History Matching - A Case Study of a Very Constrained Problem with Intensive Pressure and Saturation Data.

2006 ◽  
Author(s):  
Shawket G. Ghedan ◽  
Adrian P. Gibson ◽  
Ilhan Sener ◽  
Ozgur Eylem Gunal ◽  
Alexander Diab ◽  
...  
Keyword(s):  
History Matching ◽  
Constrained Problem ◽  
Assisted History Matching

History Matching in Two-Phase Petroleum Reservoirs

1980 ◽  
Vol 20 (06) ◽  
pp. 521-532 ◽  
Author(s):  
A.T. Watson ◽  
J.H. Seinfeld ◽  
G.R. Gavalas ◽  
P.T. Woo
Keyword(s):  
Optimal Control ◽  
Parameter Estimation ◽  
Objective Function ◽  
History Matching ◽  
Computing Time ◽  
Absolute Permeability ◽  
Two Phase ◽  
Automatic History Matching ◽  
Control Approach ◽  
First Order

Abstract An automatic history-matching algorithm based onan optimal control approach has been formulated forjoint estimation of spatially varying permeability andporosity and coefficients of relative permeabilityfunctions in two-phase reservoirs. The algorithm usespressure and production rate data simultaneously. The performance of the algorithm for thewaterflooding of one- and two-dimensional hypotheticalreservoirs is examined, and properties associatedwith the parameter estimation problem are discussed. Introduction There has been considerable interest in thedevelopment of automatic history-matchingalgorithms. Most of the published work to date onautomatic history matching has been devoted tosingle-phase reservoirs in which the unknownparameters to be estimated are often the reservoirporosity (or storage) and absolute permeability (ortransmissibility). In the single-phase problem, theobjective function usually consists of the deviationsbetween the predicted and measured reservoirpressures at the wells. Parameter estimation, orhistory matching, in multiphase reservoirs isfundamentally more difficult than in single-phasereservoirs. The multiphase equations are nonlinear, and in addition to the porosity and absolutepermeability, the relative permeabilities of each phasemay be unknown and subject to estimation. Measurements of the relative rates of flow of oil, water, and gas at the wells also may be available forthe objective function. The aspect of the reservoir history-matchingproblem that distinguishes it from other parameterestimation problems in science and engineering is thelarge dimensionality of both the system state and theunknown parameters. As a result of this largedimensionality, computational efficiency becomes aprime consideration in the implementation of anautomatic history-matching method. In all parameterestimation methods, a trade-off exists between theamount of computation performed per iteration andthe speed of convergence of the method. Animportant saving in computing time was realized insingle-phase automatic history matching through theintroduction of optimal control theory as a methodfor calculating the gradient of the objective functionwith respect to the unknown parameters. Thistechnique currently is limited to first-order gradientmethods. First-order gradient methods generallyconverge more slowly than those of higher order.Nevertheless, the amount of computation requiredper iteration is significantly less than that requiredfor higher-order optimization methods; thus, first-order methods are attractive for automatic historymatching. The optimal control algorithm forautomatic history matching has been shown toproduce excellent results when applied to field problems. Therefore, the first approach to thedevelopment of a general automatic history-matchingalgorithm for multiphase reservoirs wouldseem to proceed through the development of anoptimal control approach for calculating the gradientof the objective function with respect to theparameters for use in a first-order method. SPEJ P. 521＾

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