Abstract
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.
Highlights
The data analytic lifecycle, in relation to big data problems and data science projects, is classified into six phases (EMC 2015)
The primary activity in this phase in exploratory data analysis (EDA) with the aim of examining relationships among variables and selecting those variables that are of interest in the project and those that show some promise during the EDA process for further consideration
To build spatio-temporal models for each formation, we develop a workflow for each method that would be used across each formation
Summary
The data analytic lifecycle, in relation to big data problems and data science projects, is classified into six phases (EMC 2015) These consist of discovery, data preparation, model planning, model building, communication of results, and operationalization. The primary activity in this phase in exploratory data analysis (EDA) with the aim of examining relationships among variables and selecting those variables that are of interest in the project and those that show some promise during the EDA process for further consideration. This phase can suggest appropriate models for consideration during the model building phase. 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)
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