Hydraulic Fracture and Reservoir Characterization for Determining Optimal Well Spacing in the Marcellus Shale

Pankaj, Piyush; Shukla, Priyavrat; Kavousi, Payam; Carr, Timothy R.

doi:10.2118/191802-ms

Cited by 2 publications

(1 citation statement)

References 15 publications

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“…Such a trend is also observed in previous studies [8,[35][36][37] that EUR augments as WPS increases and after a threshold, EUR no longer increases. As discussed in Zhu, Forrest [8] and Pankaj, and Shukla [38], the threshold is believed to highly depend on the uncovered region between stimulated wells mainly concerning with fracture half length. Moreover, initial rock quality, which also contributes to the production, should impact the threshold.…”

Section: Lateral Spacing Sensitivitymentioning

confidence: 96%

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: Lateral Spacing Sensitivitymentioning

confidence: 96%

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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Finding a Trend Out of Chaos, A Machine Learning Approach for Well Spacing Optimization

Davani

et al. 2020

SPE Annual Technical Conference and Exhibition

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Data-driven decisions powered by machine-learning methods are increasing in popularity when it comes to optimizing field development in unconventional reservoirs. However, since well performance is impacted by many factors (e.g., geological characteristics, completion design, well design, etc.), the challenge is uncovering trends from all the noise. By leveraging basin-level knowledge captured by big data sculpting, integrating private and public data with the use of uncertainty quantification, Augmented AI (a combination of expert-based knowledge and advanced AI frameworks) can provide quick and science-based answers for well spacing and fracking optimization and assess the full potential of an asset in unconventional reservoirs. Augmented AI is artificial intelligence powered by engineering wisdom. The Augmented AI workflow starts with data sculpting, which includes information retrieval, data cleaning and standardization, and finally a smart, deep and systematic data QC. Feature engineering generates all the relevant parameters going into the machine learning model—over 50 features have been generated for this work and categorized. The final step is to perform model tuning and ensemble, evaluating the model robustness, generating model explanation and uncertainty quantification. Augmented AI adopts an iterative machine learning modeling approach. This approach combines new and innovative engineering and G&G workflows with data-driven models so that a deep understanding of the field behavior can be developed. Loops from feature selection to model tuning are used until good model results are achieved. The loop is automated using Bayesians optimization. All machine learning models have different strengths and weaknesses for prediction. Instead of manually determining which machine learning model to use, this approach uses an adaptive ensemble machine learning approach that is a stacking algorithm that combines multiple regression models via a second level machine learning model. It smartly aggregates opinions from different models with reduced variance and better robustness. Augmented AI has been applied in unconventional reservoirs with great results. A case study in Midland Basin is presented in this paper. Domain-induced feature engineering was performed to obtain important features for predicting well performance, and initial feature selection was conducted using feature correlation analysis. A trusted and explainable ML model was built and enhanced with uncertainty quantification. After running several sensitivity analyses, Augmented AI optimized the attributes of interest, then vetted the outcome, generating a report and visualizing the results. In addition, further information about the direct impact of well spacing on EUR was deconvoluted from other parameters using an ML explanation technique for Wolfcamp Formation in Permian Basin and subsequently well spacing optimization was presented for the case study in Midland Basin. An innovative model was created using Augmented AI to optimize well spacing, leveraging big data sculpting, domain and physics-induced feature engineering, and machine learning. The learning was transferred from the basin model to the specific region of interest. Augmented AI provides efficient and systematic private data organization, an explainable machine learning model, robust production forecast with quantified uncertainty and well spacing and frac parameters optimization. Augmented AI models are already built for major basins such as Midland and Delaware basins. The learning and knowledge of the model can be transferred to any region in a basin and can be refined using more accurate private data. This allows conclusions to be drawn even with a limited number of wells.

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Hydraulic Fracture and Reservoir Characterization for Determining Optimal Well Spacing in the Marcellus Shale

Cited by 2 publications

References 15 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

Finding a Trend Out of Chaos, A Machine Learning Approach for Well Spacing Optimization

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