EUR Assessment of Unconventional Assets Using Parallelized History Matching Workflow together with RML Method

Chen, Chaohui; Li, Ruijian; Gao, Guohua; Vink, Jeroen C.; Cao, Richard

doi:10.15530/urtec-2016-2429986

Cited by 14 publications

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“…Traditional optimization methods only locate a single optimal solution, and they are referred to as single-thread optimization methods in this paper. It is unacceptably expensive to use a singlethread optimization method to locate multiple optimal solutions on the Pareto front or generate multiple RML samples [24]. To overcome the limitations of single-thread optimization methods, Gao et al [17] developed a local-search distributed Gauss-Newton (DGN) DFO method to find multiple best matches concurrently.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Trust Region Subproblem Solvers for Limited-Memory Distributed BFGS Optimization Method

Gao

Florez

Vink

et al. 2021

Front. Appl. Math. Stat.

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The limited-memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) optimization method performs very efficiently for large-scale problems. A trust region search method generally performs more efficiently and robustly than a line search method, especially when the gradient of the objective function cannot be accurately evaluated. The computational cost of an L-BFGS trust region subproblem (TRS) solver depend mainly on the number of unknown variables (n) and the number of variable shift vectors and gradient change vectors (m) used for Hessian updating, with m << n for large-scale problems. In this paper, we analyze the performances of different methods to solve the L-BFGS TRS. The first method is the direct method using the Newton-Raphson (DNR) method and Cholesky factorization of a dense n × n matrix, the second one is the direct method based on an inverse quadratic (DIQ) interpolation, and the third one is a new method that combines the matrix inversion lemma (MIL) with an approach to update associated matrices and vectors. The MIL approach is applied to reduce the dimension of the original problem with n variables to a new problem with m variables. Instead of directly using expensive matrix-matrix and matrix-vector multiplications to solve the L-BFGS TRS, a more efficient approach is employed to update matrices and vectors iteratively. The L-BFGS TRS solver using the MIL method performs more efficiently than using the DNR method or DIQ method. Testing on a representative suite of problems indicates that the new method can converge to optimal solutions comparable to those obtained using the DNR or DIQ method. Its computational cost represents only a modest overhead over the well-known L-BFGS line-search method but delivers improved stability in the presence of inaccurate gradients. When compared to the solver using the DNR or DIQ method, the new TRS solver can reduce computational cost by a factor proportional to n2/m for large-scale problems.

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Section: Introductionmentioning

confidence: 99%

Performance Analysis of Trust Region Subproblem Solvers for Limited-Memory Distributed BFGS Optimization Method

Gao

Florez

Vink

et al. 2021

Front. Appl. Math. Stat.

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Distributed quasi-Newton derivative-free optimization method for optimization problems with multiple local optima

et al. 2021

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Maximizing the Value of Unconventional Liquid Rich Shale Development With Integrated Cross-Discipline Approach

Cao

Blount

2016

Day 1 Wed, September 21, 2016

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The unconventional Liquid Rich Shale (LRS) or light tight oil (LTO) plays are attractive but operationally challenging. The Permian-aged Bone Spring formation in the Delaware Basin is one of the earliest liquid-rich shale plays, today with hundreds of horizontal producers with greater than ten years of production history. The learnings of an integrated cross discipline approach for optimized field development can be very useful and applied to other plays. An integrated cross discipline approach has been developed to achieve deep technical understanding and optimum field development. In this cross discipline work flow, static data, such as geological structure, turbidite sand depositional thickness and extent, and petrophysical evaluation of each individual sand unit, such as SoPhiH (and other properties) are combined with engineering and dynamic data, such as completion information, production analysis, PVT, water cut, and DCA to optimize the Bone Spring development for improving the profitability and maximizing the value of Permian Asset. The Permian 3rd Bone Spring and Wolfcamp sand unit is a complex turbidite tight sand system which spans southeast New Mexico into West Texas. It consists of mixed lithology with sand/silt units separated by shale zones. Initial testing of horizontal wells in the 3rd Bone Spring sand unit began in late 2006 with 400+ wells drilled to date. The high initial reservoir pressure gradient, relatively high permeability in the sand compared to shale, and volatile/black oil in the reservoir provide the high potential for the 3rd Bone Spring Sand development. The multistacked hybrid reservoirs and the heterogeneous nature of subsurface properties also pose challenges in the development of these reservoirs. There are multiple sand units identified within the core area of Shell leasehold. The core development area has been subdivided based on geology (structure and faulting), fluid properties, and production performance.

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EUR Assessment of Unconventional Assets Using Parallelized History Matching Workflow together with RML Method

Cited by 14 publications

References 0 publications

Performance Analysis of Trust Region Subproblem Solvers for Limited-Memory Distributed BFGS Optimization Method

Performance Analysis of Trust Region Subproblem Solvers for Limited-Memory Distributed BFGS Optimization Method

Distributed quasi-Newton derivative-free optimization method for optimization problems with multiple local optima

Maximizing the Value of Unconventional Liquid Rich Shale Development With Integrated Cross-Discipline Approach

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