Hybrid Physics and Data-Driven Modeling for Unconventional Field Development - Onshore US Basin Case Study

Park, Jaeyoung; Datta‐Gupta, Akhil; Singh, Ajay; Sankaran, Sathish

doi:10.15530/urtec-2020-2573

Cited by 5 publications

(2 citation statements)

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“…It is important to understand how the reservoir has been depleted especially when it comes to placing child wells. This is because depending on the degree of the reservoir depletion for the child well, the child well might experience a significant production degradation [33].…”

Section: Geofluidsmentioning

confidence: 99%

Stimulated Reservoir Volume Characterization and Optimum Lateral Well Spacing Study of Two-Well Pad: Midland Basin Case Study

Park

Janova²

2020

Geofluids

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This paper introduces a flow simulation-based reservoir modeling study of a two-well pad with long production history and identical completion parameters in the Midland Basin. The study includes building geologic model, history matching, well performance prediction, and finding optimum lateral well spacing in terms of oil volume and economic metrics. The reservoir model was constructed based on a geologic model, integrating well logs, and core data near the target area. Next, a sensitivity analysis was performed on the reservoir simulation model to better understand influential parameters on simulation results. The following history matching was conducted with the satisfactory quality, less than 10% of global error, and after the model calibration ranges of history matching parameters have substantially reduced. The population-based history matching algorithm provides the ensemble of the history-matched model, and the top 50 history-matched models were selected to predict the range of Estimate Ultimate Recovery (EUR), showing that P50 of oil EUR is within the acceptable range of the deterministic EUR estimates. With the best history-matched model, we investigated lateral well spacing sensitivity of the pad in terms of the maximum recovery volume and economic benefit. The results show that, given the current completion design, the well spacing tighter than the current practice in the area is less effective regarding the oil volume recovery. However, economic metrics suggest that the additional monetary value can be realized with 150% of current development assumption. The presented workflow provides a systematic approach to find the optimum lateral well spacing in terms of volume and economic metrics per one section given economic assumptions, and the workflow can be readily repeated to evaluate spacing optimization in other acreage.

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Section: Geofluidsmentioning

confidence: 99%

Stimulated Reservoir Volume Characterization and Optimum Lateral Well Spacing Study of Two-Well Pad: Midland Basin Case Study

Park

Janova²

2020

Geofluids

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“…Another study presented a hybrid approach in which the authors employed the reservoir simulation input and output to build an ML model for unconventional field development. The method gives reliable predictions and is much faster than commercial numerical simulation, ( Park et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

A novel methodology for fast reservoir simulation of single-phase gas reservoirs using machine learning

Bhattacharyya

Vyas

2022

Heliyon

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Investigating the capability of data-driven proxy models as solution for reservoir geological uncertainty quantification

Kolajoobi

Haddadpour

Niri

2021

Journal of Petroleum Science and Engineering

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Hybrid Physics and Data-Driven Modeling for Unconventional Field Development - Onshore US Basin Case Study

Cited by 5 publications

References 0 publications

Stimulated Reservoir Volume Characterization and Optimum Lateral Well Spacing Study of Two-Well Pad: Midland Basin Case Study

Stimulated Reservoir Volume Characterization and Optimum Lateral Well Spacing Study of Two-Well Pad: Midland Basin Case Study

A novel methodology for fast reservoir simulation of single-phase gas reservoirs using machine learning

Investigating the capability of data-driven proxy models as solution for reservoir geological uncertainty quantification

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