Spatio-Temporal Models for Big Data and Applications on Unconventional Production Evaluation

Wigwe, M. E.; Bougre, E. S.; Watson, Marshall; Giussani, Alberto

doi:10.15530/urtec-2020-2855

Cited by 4 publications

(2 citation statements)

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“…Operators have increased the lateral length, number of stages, and amount of hydraulic fracturing fluids on unconventional well completion [22]. For Marcellus shale and Eagle Ford shale, the number of stages increased from 10 in 2008 to 16 in 2012 [23]; for Bakken shale, the number of stages increased from 20 in 2008 to 94 in 2019 [24]. The length of the horizontal wells is usually long, 5000-10,000 ft, and a hydraulic fracturing operation can have as many as 30-40 fracturing stages [25].…”

Section: Production-related Attributesmentioning

confidence: 99%

Probabilistic Prediction of Multi-Wells Production Based on Production Characteristics Analysis Using Key Factors in Shale Formations

2021

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In this study, we propose a novel workflow to predict the production of existing and new multi-wells. To perform reliable production forecasting on heterogeneous shale formations, the features of these formations must be analyzed by classifying the formations into various groups; the groups have different production characteristics depending on the key factors that affect the shale formation. In addition, the limited data obtained from nearby existing multi-wells should be used to estimate the production of new wells. The key factors that affect shale formation were derived from the correlation and principal component analysis of available production-related attributes. The production of existing wells was estimated by classifying them into groups based on their production characteristics. These classified groups also identified the relationship between hydraulic fracturing design factors and productivity. To estimate the production of new wells (blind wells), we generated groups with different production characteristics and leveraged their features to estimate the production. Probabilistic values of the group features were entered into the input layer of the artificial neural network model to consider the variation in the production of shale formations. All the estimated productions exhibited less error than the previous analytical results, suggesting the utilization potential of the proposed workflow.

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Section: Production-related Attributesmentioning

confidence: 99%

Probabilistic Prediction of Multi-Wells Production Based on Production Characteristics Analysis Using Key Factors in Shale Formations

2021

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“…The model planning phase involves developing a workflow for building the model. In the model-building phase, we construct the model using the information from previous phases, and the workflow developed in the model planning phase (Wigwe et al 2020).…”

Section: Introductionmentioning

confidence: 99%

Comparative evaluation of multi-basin production performance and application of spatio-temporal models for unconventional oil and gas production prediction

Wigwe

Bougre

Watson

et al. 2020

J Petrol Explor Prod Technol

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Modern data analytic techniques, statistical and machine-learning algorithms have received widespread applications for solving oil and gas problems. As we face problems of parent–child well interactions, well spacing, and depletion concerns, it becomes necessary to model the effect of geology, completion design, and well parameters on production using models that can capture both spatial and temporal variability of the covariates on the response variable. We accomplish this using a well-formulated spatio-temporal (ST) model. In this paper, we present a multi-basin study of production performance evaluation and applications of ST models for oil and gas data. We analyzed dataset from 10,077 horizontal wells from 2008 to 2019 in five unconventional formations in the USA: Bakken, Marcellus, Eagleford, Wolfcamp, and Bone Spring formations. We evaluated well production performance and performance of new completions over time. Results show increased productivity of oil and gas since 2008. Also, the Bakken wells performed better for the counties evaluated. We present two methods for fitting spatio-temporal models: fixed rank kriging and ST generalized additive models using thin plate and cubic regression splines as basis functions in the spline-based smooths. Results show a significant effect on production by the smooth term, accounting for between 60 and 95% of the variability in the six-month production. Overall, we saw a better production response to completions for the gas formations compared to oil-rich plays. The results highlight the benefits of spatio-temporal models in production prediction as it implicitly accounts for geology and technological changes with time.

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Twelve years of unconventional oil and gas development: production performance and economic analysis

Wigwe

Giussani

Watson

2021

Int J Energy Environ Eng

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Spatio-Temporal Models for Big Data and Applications on Unconventional Production Evaluation

Cited by 4 publications

References 0 publications

Probabilistic Prediction of Multi-Wells Production Based on Production Characteristics Analysis Using Key Factors in Shale Formations

Probabilistic Prediction of Multi-Wells Production Based on Production Characteristics Analysis Using Key Factors in Shale Formations

Comparative evaluation of multi-basin production performance and application of spatio-temporal models for unconventional oil and gas production prediction

Twelve years of unconventional oil and gas development: production performance and economic analysis

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