Population-based sampling methods for geological well testing

Hamdi, Hamidreza; Hajizadeh, Yasin; Sousa, Mário Costa

doi:10.1007/s10596-015-9522-7

Cited by 12 publications

(2 citation statements)

References 54 publications

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“…The maximum expected improvement seeks to maximize the improvement we get if we sample from a new location x i . For maximizing the EI, we use the DE/Best variant of the Differential Evolution (DE) algorithm which is an efficient population-based optimization technique (Storn and Price, 1995;Hamdi et al, 2015). Although, DE requires a large number of function evaluations, this would not create any problem for maximizing the EI as this analytical function is very cheap to evaluate.…”

Section: Maximum Expected Improvement (Mei)mentioning

confidence: 99%

Gaussian Process for Uncertainty Quantification of Reservoir Models

2015

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Reservoir history matching is a computationally expensive process, which requires multiple simulation runs. Therefore, there is a constant quest for more efficient sampling algorithms that can provide an ensemble of equally-good history matched models with a diverse range of predictions using fewer simulations. We introduce a novel stochastic Gaussian Process (GP) for assisted history matching where realizations are considered to be Gaussian random variables. The GP benefits from a small initial population and selects the next best possible samples by maximizing the expected improvement (EI). The maximization of EI function is computationally cheap and is performed by the Differential Evolution (DE) algorithm. The algorithm is successfully applied to a structurally complex faulted reservoir with 12 unknown parameters, 8 production and 4 injection wells. We show that the GP algorithm with EI maximization can significantly reduce the number of required simulations for history matching. The ensemble is then used to estimate the posterior distributions by performing the Markov chain Monte Carlo (McMC) using a cross-validated GP model. The hybrid workflow presents an efficient and computationally-cheap mechanism for history matching and uncertainty quantification of complex reservoir models.

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Section: Maximum Expected Improvement (Mei)mentioning

confidence: 99%

Gaussian Process for Uncertainty Quantification of Reservoir Models

2015

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“…The proxy model has been utilized in various reservoir studies and enhanced oil recovery (EOR) modeling applications, including in the optimization of the oil flow rate [18,19], waterflooding processes [20][21][22], gas flooding processes [23], steam injection [11,[24][25][26], chemical flooding [14], foam flooding [27], and history matching [16,28,29]. Proxy models have been successfully utilized in reservoir studies, such as the application of a second-degree polynomial equation [24,[30][31][32][33], Kriging algorithms [16,17,24,34], multivariate adaptive regression splines [35][36][37], response surface methodology [38,39], and artificial neural network algorithms [16,19,31,40].…”

Section: Introductionmentioning

confidence: 99%

Leveraging Designed Simulations and Machine Learning to Develop a Surrogate Model for Optimizing the Gas–Downhole Water Sink–Assisted Gravity Drainage (GDWS-AGD) Process to Improve Clean Oil Production

Al-Mudhafar,

Rao,

Wojtanowicz

2024

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The Gas and Downhole Water Sink–Assisted Gravity Drainage (GDWS-AGD) process addresses gas flooding limitations in reservoirs surrounded by infinite-acting aquifers, particularly water coning. The GDWS-AGD technique reduces water cut in oil production wells, improves gas injectivity, and optimizes oil recovery, especially in reservoirs with high water coning. The GDWS-AGD process installs two 7-inch production casings bilaterally. Then, two 2-3/8-inch horizontal tubings are completed. One tubing produces oil above the oil–water contact (OWC) area, while the other drains water below it. A hydraulic packer in the casing separates the two completions. The water sink completion uses a submersible pump to prevent water from traversing the oil column and entering the horizontal oil-producing perforations. To improve oil recovery in the heterogeneous upper sandstone pay zone of the South Rumaila oil field, which has a strong aquifer and a large edge water drive, the GDWS-AGD process evaluation was performed using a compositional reservoir flow model in a 10-year prediction period in comparison to the GAGD process. The results show that the GDWS-AGD method surpasses the GAGD by 275 million STB in cumulative oil production and 4.7% in recovery factor. Based on a 10-year projection, the GDWS-AGD process could produce the same amount of oil in 1.5 years. In addition, the net present value (NPV) given various oil prices (USD 10–USD 100 per STB) was calculated through the GAGD and GDWS-AGD processes. The GDWS-AGD approach outperforms GAGD in terms of NPV across the entire range of oil prices. The GAGD technique became uneconomical when oil prices dropped below USD 10 per STB. Design of Experiments–Latin Hypercube Sampling (DoE-LHS) and radial basis function neural networks (RBF-NNs) were used to determine the optimum operational decision variables that influence the GDWS-AGD process’s performance and build the proxy metamodel. Decision variables include well constraints that control injection and production. The optimum approach increased the recovery factor by 1.7525% over the GDWS-AGD process Base Case. With GDWS-AGD, water cut and coning tendency were significantly reduced, along with reservoir pressure, which all led to increasing gas injectivity and oil recovery. The GDWS-AGD technique increases the production of oil and NPV more than the GAGD process. Finally, the GDWS-AGD technique offers significant improvements in oil recovery and income compared to GAGD, especially in reservoirs with strong water aquifers.

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2022

Artificial Intelligence and Data Analytics for Energy Exploration and Production

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Population-based sampling methods for geological well testing

Cited by 12 publications

References 54 publications

Gaussian Process for Uncertainty Quantification of Reservoir Models

Gaussian Process for Uncertainty Quantification of Reservoir Models

Leveraging Designed Simulations and Machine Learning to Develop a Surrogate Model for Optimizing the Gas–Downhole Water Sink–Assisted Gravity Drainage (GDWS-AGD) Process to Improve Clean Oil Production

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