Reservoir development optimization under uncertainty for infill well placement in brownfield redevelopment

Hutahaean, Junko; Demyanov, Vasily; Christie, Mike

doi:10.1016/j.petrol.2018.12.043

Cited by 35 publications

(3 citation statements)

References 32 publications

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“…One of the most common methods of oil production intensification in the Perm Region is hydraulic fracturing [1], including its modification -acid hydraulic fracturing. This technology is carried out on carbonate reservoirs, which are characterized by heterogeneity of reservoir properties and low productivity coefficients [2][3][4]. During acid hydraulic fracturing, a highly viscous liquid is injected into the formation under high pressure to form a crack and fill highly permeable channels, as well as a hydrochloric acid solution, when interacting with which the crack surface becomes rough.…”

Section: Introductionmentioning

confidence: 99%

Possibility of using machine learning algorithms to assess the effectiveness of acid hydraulic fracturing

Kochnev

Torsunova

Boyarov

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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At the fields of the Perm Territory, there is a significant application of hydraulic fracturing technology to stimulate the production of carbonate reservoirs. The analysis of the efficiency of the acid hydraulic fracturing technology by the example of the fields of the Perm Territory is presented in the article. The effectiveness of the technology is largely determined by the geological and physical conditions of the fields. Using the methods of mathematical statistics, the main geological and physical parameters which most significantly affect the effectiveness of measures for acid hydraulic fracturing have been identified. A multiple regression model was built to find out the complex influence of the parameters on the increase in oil production after the measures. Various machine learning algorithms have been used to predict the increase in oil and fluid input rates after acid fracturing. Different algorithms showed different results. For the problems of forecasting oil production after the application of acid fracturing, the most successful one was the support vector method.

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Section: Introductionmentioning

confidence: 99%

Possibility of using machine learning algorithms to assess the effectiveness of acid hydraulic fracturing

Kochnev

Torsunova

Boyarov

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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“…Several gradient-based methods have been devised and utilized to solve different optimization problems such as well placement and control problems (Hutahaean et al, 2019;Liu et al, 2019;Sun et al, 2019;Epelle and Gerogiorgis, 2020). In terms of efficiency and accuracy, the adjoint method is ranked number one on the list of gradient-based methods because of its efficient and accurate gradient computation.…”

Section: Introductionmentioning

confidence: 99%

“…Theoretically, it has been proven by using the steepest descent scheme that a small difference in gradient computation exists between StoSAG, EnOpt, and SPSA (as well as its variants such as Gaussian-SPSA) methods. These methods have gained popularity recently due to their ability to incorporate uncertainty represented by multiple realizations of the reservoir model in their approximate gradient computation (Hutahaean et al, 2019;.…”

Section: Introductionmentioning

confidence: 99%

Efficient Optimization and Robust Value Quantification of Enhanced Oil Recovery Strategies

Oguntola¹

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With an increasing demand for hydrocarbon reservoir produces such as oil, etc., and difficulties in finding green oil fields, the use of Enhanced Oil Recovery (EOR) methods such as polymer, Smart water, and solvent flooding for further development of existing fields can not be overemphasized. For reservoir profitability and reduced environmental impact, it is crucial to consider appropriate well control settings of EOR methods for given reservoir characterization. Moreover, finding appropriate well settings requires solving a constrained optimization problem with suitable numerical solution methods. Conventionally, the solution method requires many iterations involving several computationally demanding function evaluations before convergence to the appropriate near optimum. The major subject of this thesis is to develop an efficient and accurate solution method for constrained optimization problems associated with EOR methods for their value quantifications and ranking in the face of reservoir uncertainties. The first contribution of the thesis develops a solution method based on the inexact line search method (with Ensemble Based Optimization (EnOpt) for approximate gradient computation) for robust constrained optimization problems associated with polymer, Smart water, and solvent flooding. Here, the objective function is the expectation of the Net Present Value (NPV) function over given geological realizations. For a given set of well settings, the NPV function is defined based on the EOR simulation model, which follows from an appropriate extension of the black-oil model. The developed solution method is used to find the economic benefits and also the ranking of EOR methods for different oil reservoirs developed to mimic North Sea reservoirs. Performing the entire optimization routine in a transformed domain along with truncations has been a common practice for handling simple linear constraints in reservoir optimization. Aside from the fact that this method has a negative impact on the quality of gradient computation, it is complicated to use for non-linear constraints. The second contribution of this thesis proposes a technique based on the exterior penalty method for handling general linear and non-linear constraints in reservoir optimization problems to improve gradient computation quality by the EnOpt method for efficient and improved optimization algorithm. Because of the computationally expensive NPV function due to the costly reservoir simulation of EOR methods, the solution method for the underlying EOR optimization problem becomes inefficient, especially for large reservoir problems. To speedup the overall computation of the solution method, this thesis introduces a novel full order model (FOM)-based certified adaptive machine learning optimization procedures to locally approximate the expensive NPV function. A supervised feedforward deep neural network (DNN) algorithm is employed to locally create surrogate model. In the FOM-based optimization algorithm of this study, several FOM NPV function evaluations are required by the EnOpt method to approximate the gradient function at each (outer) iteration until convergence. To limit the number FOM-based evaluations, we consider building surrogate models locally to replace the FOM based NPV function at each outer iteration and proceed with an inner optimization routine until convergence. We adapt the surrogate model using some FOM-based criterion where necessary until convergence. The demonstration of methodology for polymer optimization problem on a benchmark model results in an improved optimum and found to be more efficient compared to using the full order model optimization procedures.

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Field Development Optimization Under Geological Uncertainty

Yousefzadeh¹,

Kazemi²,

Ahmadi³

et al. 2023

SpringerBriefs in Petroleum Geoscience &Amp; Engineering

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Reservoir development optimization under uncertainty for infill well placement in brownfield redevelopment

Cited by 35 publications

References 32 publications

Possibility of using machine learning algorithms to assess the effectiveness of acid hydraulic fracturing

Possibility of using machine learning algorithms to assess the effectiveness of acid hydraulic fracturing

Efficient Optimization and Robust Value Quantification of Enhanced Oil Recovery Strategies

Field Development Optimization Under Geological Uncertainty

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