Streamline Technology: Reservoir History Matching and Forecasting = Its Success, Limitations, and Future

Baker, Richard

doi:10.2118/01-04-das

Cited by 26 publications

(17 citation statements)

References 27 publications

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“…Anterion [2] first applied it to the oil industry to diminish computation time in inverse problems for reservoir simulation. Since then, more applications using HM to get reservoir characteristics have emerged [3,4,5]. HM sequentially cuts down the initial input space using an implausibility threshold that includes various of uncertainty sources.…”

Section: Introductionmentioning

confidence: 99%

Sampling Schemes for History Matching Using Subset Simulation

Gong¹,

O²,

Beer³

2017

Proceedings of the 2nd International Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNCECO

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Abstract. History Matching (HM) is a form of model calibration suitable for high-dimensional and computationally expensive numerical models. It sequentially cuts down the input space to find the non-implausible domain that provides a reasonable match between the output and experimental data. The non-implausible domain can be orders of magnitude smaller than the original input space and it can have a complex topology. This leads to one of the most challenging open problems in implementing HM, namely, the efficient generation of samples in the non-implausible set. Previous work has shown that Subset Simulation can be used to solve this problem. Unlike Direct Monte Carlo, Subset Simulation progressively decomposes a rare event (here is the non-implausible set), which has very small failure probabilities, into sequential less rare nested events. The original Subset Simulation uses a Modified Metropolis algorithm to generate the conditional samples that belong to intermediate less rare failure events. Generating samples moving forwards to the target space is the heart for Subset Simulation. This work considers different sampling strategies to generate samples and compares their performance in the context of expensive model calibration. A numerical example is provided to show the potential of HM using different Subset Simulation sampling schemes.

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Section: Introductionmentioning

confidence: 99%

Sampling Schemes for History Matching Using Subset Simulation

Gong¹,

O²,

Beer³

2017

Proceedings of the 2nd International Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNCECO

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“…Time of flight is the time taken for a flux line, or a pressure response, to move from one point to another. In this way, fluid leaving an injector for example can be tracked, and fluid velocity can be monitored, particularly in regions of higher permeability, where a fast-moving tracer will lead for instance to early arrival [5]. The base case assumption in SL simulation is that the fluid compressibility is zero, since water is used as the displacing fluid.…”

Section: Introductionmentioning

confidence: 99%

Application of Streamline Simulation to Gas Displacement Processes

2017

IJM

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“…History matching and reservoir forecasting are key areas in the oil industry for retrieving petrophysical parameters, matching reservoir models to the real reservoir data, and providing forecasts for proper business and reservoir-management decisions (Baker 2001). Reservoir history matching has been a common industry practice for many decades but has recently attracted even more prominence with increasing exploration costs and oil prices (Oliver et al 2008).…”

Section: Introductionmentioning

confidence: 99%

Synergizing Crosswell Seismic and Electromagnetic Techniques for Enhancing Reservoir Characterization

2016

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SummaryIncreasing complexity of hydrocarbon projects and the request for higher recovery rates have driven the oil-and-gas industry to look for a more-detailed understanding of the subsurface formation to optimize recovery of oil and profitability. Despite the significant successes of geophysical techniques in determining changes within the reservoir, the benefits from individually mapping the information are limited. Although seismic techniques have been the main approach for imaging the subsurface, the weak density contrast between water and oil has made electromagnetic (EM) technology an attractive complement to improve fluid distinction, especially for high-saline water. This crosswell technology assumes greater importance for obtaining higher-resolution images of the interwell regions to more accurately characterize the reservoir and track fluid-front developments. In this study, an ensemble-Kalman-based history-matching framework is proposed for directly incorporating crosswell time-lapse seismic and EM data into the history-matching process. The direct incorporation of the time-lapse seismic and EM data into the history-matching process exploits the complementarity of these data to enhance subsurface characterization, to incorporate interwell information, and to avoid biases that may be incurred from separate inversions of the geophysical data for attributes. An extensive analysis with 2D and realistic 3D reservoirs illustrates the robustness and enhanced forecastability of critical reservoir variables. The 2D reservoir provides a better understanding of the connection between fluid discrimination and enhanced history matches, and the 3D reservoir demonstrates its applicability to a realistic reservoir. Historymatching enhancements (in terms of reduction in the historymatching error) when incorporating both seismic and EM data averaged approximately 50% for the 2D case, and approximately 30% for the 3D case, and permeability estimates were approximately 25% better compared with a standard history matching with only production data.

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Streamline Technology: Reservoir History Matching and Forecasting = Its Success, Limitations, and Future

Cited by 26 publications

References 27 publications

Sampling Schemes for History Matching Using Subset Simulation

Sampling Schemes for History Matching Using Subset Simulation

Application of Streamline Simulation to Gas Displacement Processes

Synergizing Crosswell Seismic and Electromagnetic Techniques for Enhancing Reservoir Characterization

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