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

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.

A data-driven optimization model for the workover rig scheduling problem: Case study in an oil company

“…The instance generator uses historical data to generate instances for the optimization, as described in Fig. 10 Based on Wigwe et al (2020), the wells' oil production (in bbl, barrels) were generated randomly according to their type, using the Gamma distribution, as follows in Eqs. ( 30) and (31):…”

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

“…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 Characteristics and Main Controlling Factors of the Upper Paleozoic Sandstone Reservoirs in Dongpu Depression, Bohai Bay Basin

“…With the continuous growth in the global demand for oil and gas and the continuous decline in the replacement rates of conventional oil and gas reserves, unconventional oil and gas products with major resource exploration potential have gradually become the focus of oil and gas research for the past 10 years (Davies et al, 2014;Zou et al, 2014;Clarkson and Qanbari, 2015;Song et al, 2017;Yang et al, 2017;Romero-Sarmiento et al, 2017;Lei et al, 2018;Wigwe et al, 2020;Katiyar et al, 2021;Bandara et al, 2021). The proportion of unconventional oil and gas in the world energy structure has been increasing.…”