Well placement subclustering within partially oil-saturated flow units

Oliveira, Gustavo P.; Santos, M.D.; Roemers-Oliveira, Eduardo

doi:10.1016/j.petrol.2020.107730

Journal of Petroleum Science and Engineering

2021

DOI: 10.1016/j.petrol.2020.107730

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Well placement subclustering within partially oil-saturated flow units

Gustavo P. Oliveira

M.D. Santos

Eduardo Roemers-Oliveira

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Cited by 6 publications

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“…HFUs are volumes of a reservoir with favoured ability to convey subsurface fluids. This way, flow unit models enable us to select "chunks" of geologic information around wells so that special cells-so-called maximum closeness centrality cells (MaxCs)-known a priori as flow convergence zones are used to construct input arrays with reduced dimension and high quality to describe the production mechanisms necessary to feed the network learning (Oliveira et al 2016(Oliveira et al , 2020.…”

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confidence: 99%

Cumulative oil production in flow unit-crossing wells estimated by multilayer perceptron networks

Neto

Oliveira

Magalhães

et al. 2021

J Petrol Explor Prod Technol

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Knowing the ultimate oil production in wells is a crucial point for reservoir planning and management to anticipate value for money. Commercial reservoir simulators are able to predict production curves with high confidence, but repetitive tasks in a few cases may spend a precious time of staff as well as require a large computational effort. Although artificial intelligence (AI) is providing an alternative path to the usual workflow, many commercial simulators lack robust AI algorithms. This work introduces a methodology based on a multilayer perceptron (MLP) neural network to predict the final cumulative oil production of a reservoir at vertical wells that cross hydraulic flow units (HFUs), which are volumes endowed with good flow attributes. Each well location is attached to special spots previously determined from clustering and calculation of maximum closeness centrality points (MaxCs) within a class of HFUs. The database is divided into training, validation, and testing sets organized after processing the UNISIM-I-D synthetic model, representative of the Namorado Field, Campos Basin, Brazil. The key rationale of this paper is to use the feature of MaxCs of being drivers for well placement as knowledge base to learn the production mechanisms of the oilfield. The outcomes are presented from two perspectives: an original MLP and its post-processed version. Both are compared with reservoir simulations carried out in CMG Imex$$^{\copyright }$$ © and achieve reasonable agreement. The performance is measured by root-mean-squared error (RMSE) and mean absolute scaled error (MASE) both in original and post-processed versions. We show that average RMSE and MASE values near 0.07 and 14.00, respectively, are achieved without post-processing. With post-processing, gains of up to 43% are reported for the integral oil volume.

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mentioning

confidence: 99%

Cumulative oil production in flow unit-crossing wells estimated by multilayer perceptron networks

Neto

Oliveira

Magalhães

et al. 2021

J Petrol Explor Prod Technol

View full text Add to dashboard Cite

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Estimation of distribution algorithms for well placement optimization in petroleum fields

Brum,

Coelho,

Santos

et al. 2024

Comput Geosci

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Optimal path planning for directional wells across flow units’ many-targets

Fernandes,

Coutinho,

Silva

et al. 2023

J Petrol Explor Prod Technol

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Over the past decades, directional drilling has continuously advanced to increase hydrocarbon recovery by effectively targeting high-productivity reservoirs. However, many existing approaches primarily focus on heuristic optimization algorithms. Moreover, existing models often neglect the incorporation of petrophysical attributes that can significantly impact the selection of production targets, such as the reservoir quality indicator. This article introduces a novel application of mixed-integer programming to define directional drilling paths, considering practical aspects of interest. The paths are subject to drift angle constraints and reference coordinates that align with the optimal reservoir targets. Such targets are identified using the authors’ proposed technique of maximum closeness centrality and the geologic model of hydraulic flow units. In order to evaluate the effectiveness of this approach, a realistic model of the Campos Basin in Brazil is studied. The results reveal that the highest recovery factors obtained with the proposed methodology (17%) exceed the historical average recovery factor of the studied reservoir (15.66%). We believe this study can contribute to the ongoing efforts to enhance directional drilling and maximize the production potential of offshore oil and gas reservoirs.

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Well placement subclustering within partially oil-saturated flow units

Cited by 6 publications

References 28 publications

Cumulative oil production in flow unit-crossing wells estimated by multilayer perceptron networks

Cumulative oil production in flow unit-crossing wells estimated by multilayer perceptron networks

Estimation of distribution algorithms for well placement optimization in petroleum fields

Optimal path planning for directional wells across flow units’ many-targets

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