A robust proxy for production well placement optimization problems

Pouladi, Behzad; Keshavarz, Sahar; Sharifi, Mohammad; Ahmadi, Mohammad Ali

doi:10.1016/j.fuel.2017.06.030

Cited by 57 publications

(8 citation statements)

References 28 publications

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“…The B&B method was the computationally least efficient (with the highest number of iterations required). The reduction of computational complexity using proxy models in well placement optimisation problems has also received some attention, with key contributions from Guyaguler (2002) and Pouladi et al (2017). Kriging and fast marching methods were respectively applied.…”

Section: Relevant Literaturementioning

confidence: 99%

Adjoint-based well placement optimisation for Enhanced Oil Recovery (EOR) under geological uncertainty: From seismic to production

Epelle

Gerogiorgis

2020

Journal of Petroleum Science and Engineering

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The development of new and existing oil fields is very expensive and technically challenging; this provides an important economic incentive to improve oil recovery via efficient computational optimisation. Decisions related to these activities may be significantly aided by sound and proven mathematical-oriented methods. The use of intuitive engineering judgement alone cannot guarantee sustainable profitability over long periods especially under geological (reservoir model) uncertainty. To capture the uncertainties in the subsurface geological/reservoir model in this work, geostatistical realisations of the model are obtained using available information (permeabilities and porosities). We also apply specialised algorithms within the MATLAB Reservoir Simulation Toolbox-MRST (interfaced with PETREL TM) to optimally vary the well locations and production rates, thus maximising the field's oil recovery. A new modification of an existing pseudowell-based injection well placement algorithm is presented herein with the application of adjoint-computed gradients of an auxiliary objective function (the Lorenz coefficient). The difficulty of this problem is characterised by the presence of discrete variables, nonlinear and nonconvex objective function and constraints. The developed workflow is applied to a realistic case study, for which robust optimality is demonstrated using the worst case realisation for the determination of optimal well locations and controls. A comparative investigation of optimising injection well location vs. simultaneous optimisation of injection and production well placement is also presented. In our presented case study, we further discover that increasing the optimisation search space does not necessarily guarantee improved results.

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Section: Relevant Literaturementioning

confidence: 99%

Adjoint-based well placement optimisation for Enhanced Oil Recovery (EOR) under geological uncertainty: From seismic to production

Epelle

Gerogiorgis

2020

Journal of Petroleum Science and Engineering

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“…A practical alternative is to use a proxy model, which is well suited for repeated calculations. There are two main types of surrogate models, where one is the reduced physical model (Wilson and Durlofsky, 2013;Pouladi et al, 2017), and the other one is the data-driven model (Zhou et al, 2014;Kulga et al, 2017;Wang and Chen, 2019b;Wang et al, 2021;Xue et al, 2021). The data-driven model can quickly establish a mathematical model approaching the accuracy of the numerical simulation model by sampling the reservoir numerical simulator.…”

Section: Introductionmentioning

confidence: 99%

A Novel Shale Gas Production Prediction Model Based on Machine Learning and Its Application in Optimization of Multistage Fractured Horizontal Wells

Wang

Qiao

et al. 2021

Front. Earth Sci.

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Shale gas production prediction and horizontal well parameter optimization are significant for shale gas development. However, conventional reservoir numerical simulation requires extensive resources in terms of labor, time, and computations, and so the optimization problem still remains a challenge. Therefore, we propose, for the first time, a new gas production prediction methodology based on Gaussian Process Regression (GPR) and Convolution Neural Network (CNN) to complement the numerical simulation model and achieve rapid optimization. Specifically, through sensitivity analysis, porosity, permeability, fracture half-length, and horizontal well length were selected as influencing factors. Second, the n-factorial experimental design was applied to design the initial experiment and the dataset was constructed by combining the simulation results with the case parameters. Subsequently, the gas production model was built by GPR, CNN, and SVM based on the dataset. Finally, the optimal model was combined with the optimization algorithm to maximize the Net Present Value (NPV) and obtain the optimal fracture half-length and horizontal well length. Experimental results demonstrated the GPR model had prominent modeling capabilities compared with CNN and Support Vector Machine (SVM) and achieved the satisfactory prediction performance. The fracture half-length and well length optimized by the GPR model and reservoir numerical simulation model converged to almost the same values. Compared with the field reference case, the optimized NPV increased by US$ 7.43 million. Additionally, the time required to optimize the GPR model was 1/720 of that of numerical simulation. This work enriches the knowledge of shale gas development technology and lays the foundation for realizing the scale-benefit development for shale gas, so as to realize the integration of geological engineering.

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“…In fact proxies have attracted many researchers with the hope of reducing the simulation and hence the optimization time. 8,9 However, the second group uses the mathematical programming technique. 10−12 An example is a series of studies by Tavallali, Karimi, and their co-workers; they tackled the static 13 and later the dynamic well placement problem 14,15 and formulated these problems through a framework for mixed integer nonlinear programming (MINLP) problems.…”

Section: Introductionmentioning

confidence: 99%

“…Hence, Chen et al coupled a numerical simulator with a so-called cat swarm optimization algorithm and attempted to reduce the simulation time by defining a specific analytical formula-based objective function. Similarly Pouladi et al used volumetric pressure approximation data provided by fast marching method as a proxy for reducing the computational cost. In fact proxies have attracted many researchers with the hope of reducing the simulation and hence the optimization time. , …”

Section: Introductionmentioning

confidence: 99%

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Optimal Drilling Planning by Considering the Subsurface Dynamics—Combing the Flexibilities of Modeling and a Reservoir Simulator

Tavallali

Bakhtazma

Meymandpour

et al. 2018

Ind. Eng. Chem. Res.

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Many drilling and oil companies are facing tight budgets, and a promising option for survival is to optimally revisit the drilling plans and production operations. Although there are some options for this task in the current reservoir simulators, more in-depth analysis is indeed required. Hence, in this study we borrow the flexibilities and capabilities of both modeling and a reservoir simulator to spatiotemporally plan the drillings of new injector and/or producer wells, determine the total number of new wells, and schedule the production/injection from all wells. The entire approach is both mathematically enriched and simple to use in the industry. We show that our methodology outperforms the decisions of an industrially accepted reservoir simulator in two separate examples by 4.4% and 2.4%, respectively.

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A robust proxy for production well placement optimization problems

Cited by 57 publications

References 28 publications

Adjoint-based well placement optimisation for Enhanced Oil Recovery (EOR) under geological uncertainty: From seismic to production

Adjoint-based well placement optimisation for Enhanced Oil Recovery (EOR) under geological uncertainty: From seismic to production

A Novel Shale Gas Production Prediction Model Based on Machine Learning and Its Application in Optimization of Multistage Fractured Horizontal Wells

Optimal Drilling Planning by Considering the Subsurface Dynamics—Combing the Flexibilities of Modeling and a Reservoir Simulator

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