Hierarchical benchmark case study for history matching, uncertainty quantification and reservoir characterisation

Arnold, Daniel; Demyanov, Vasily; Tatum, Dominic; Christie, Mike; Rojas, Temistocles Simon; Geiger, Sebastian; Corbett, Patrick William Michael

doi:10.1016/j.cageo.2012.09.011

Cited by 40 publications

(13 citation statements)

References 27 publications

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“…Because all the discussed features require proper interpretation from indirect measurements of the incomplete data and demand judgments based on domain knowledge, there is a need for a case study that would consider fault location, dimensions and uncertainty of network connectivity (Arnold et al, 2013;Wu and Xu, 2014). The fault control-points form fault lines, and these lines form a fault network which is able to explicitly describe the topological relationship of faults.…”

Section: Fault Network Analysismentioning

confidence: 99%

A 3D modeling approach to complex faults with multi-source data

Zou

et al. 2015

Computers & Geosciences

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Section: Fault Network Analysismentioning

confidence: 99%

A 3D modeling approach to complex faults with multi-source data

Zou

et al. 2015

Computers & Geosciences

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“…When predicting permeability from well logs, Olatunji et al (2011) adopted a type-2 fuzzy logic system which is good at handling uncertainties in measurements and data used to calibrate the parameters. Arnold et al (2013) carried out a hierarchical benchmark case study for history matching, uncertainty quantification, and reservoir characterization. Since automatic history matching has become increasingly important in the field of reservoir description, more efforts should be paid to uncertainty analyses to reduce the decision risks in the future.…”

Section: Uncertainty Analysesmentioning

confidence: 99%

A review of closed-loop reservoir management

Hou

Zhou

Zhang³

et al. 2015

Pet. Sci.

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The closed-loop reservoir management technique enables a dynamic and real-time optimal production schedule under the existing reservoir conditions to be achieved by adjusting the injection and production strategies. This is one of the most effective ways to exploit limited oil reserves more economically and efficiently. There are two steps in closed-loop reservoir management: automatic history matching and reservoir production optimization. Both of the steps are large-scale complicated optimization problems. This paper gives a general review of the two basic techniques in closed-loop reservoir management; summarizes the applications of gradient-based algorithms, gradient-free algorithms, and artificial intelligence algorithms; analyzes the characteristics and application conditions of these optimization methods; and finally discusses the emphases and directions of future research on both automatic history matching and reservoir production optimization.

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“…evolutionary algorithms, machine learning and particle swarms) are continuously adapted to reservoir simulation, and industry workflows for uncertainty quantification have been updated accordingly (e.g. Hajizadeh et al 2011;Abdollazadeh et al 2013;Arnold et al 2013;Ashraf et al 2013;Dehdari et al 2013;He & Durlofsky 2013;Park et al 2013;Peters et al 2013;El-Sheikh et al 2014). However, it is not clear how readily those new algorithms, which are commonly developed for well-known and sometimes slightly idealized benchmark problems comprising clastic reservoir models, can be applied to carbonate reservoirs.…”

Section: Background and Challengesmentioning

confidence: 99%

Fundamental controls on fluid flow in carbonates: current workflows to emerging technologies

Agar

Geiger

2014

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The introduction reviews topics relevant to the fundamental controls on fluid flow in carbonate reservoirs and to the prediction of reservoir performance. The review provides research and industry contexts for papers in this volume only. A discussion of global context and frameworks emphasizes the value yet to be captured from compare and contrast studies. Multidisciplinary efforts highlight the importance of greater integration of sedimentology, diagenesis and structural geology. Developments in analytical and experimental methods, stimulated by advances in the materials sciences, support new insights into fundamental (pore-scale) processes in carbonate rocks. Subsurface imaging methods relevant to the delineation of heterogeneities in carbonates highlight techniques that serve to decrease the gap between seismically resolvable features and well-scale measurements. Methods to fuse geological information across scales are advancing through multiscale integration and proxies. A surge in computational power over the last two decades has been accompanied by developments in computational methods and algorithms. Developments related to visualization and data interaction support stronger geoscience-engineering collaborations. High-resolution and real-time monitoring of the subsurface are driving novel sensing capabilities and growing interest in data mining and analytics. Together, these offer an exciting opportunity to learn more about the fundamental fluid-flow processes in carbonate reservoirs at the interwell scale.

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Hierarchical benchmark case study for history matching, uncertainty quantification and reservoir characterisation

Cited by 40 publications

References 27 publications

A 3D modeling approach to complex faults with multi-source data

A 3D modeling approach to complex faults with multi-source data

A review of closed-loop reservoir management

Fundamental controls on fluid flow in carbonates: current workflows to emerging technologies

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