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Abstract Predicting gas production from stimulated unconventional reservoirs has always been a challenge to oil and gas companies because the physics of fluid flow through such reservoirs is not well understood. On the other hand, modeling gas production from shale reservoirs is a complex multi-variate problem that requires proper integration of data from multiple disciplines, such as geology, petrophysics, geomechanics, completions, and surface-based measurements, etc. This study demonstrates the application of data-driven machine learning algorithms, integrating geoscientific, distributed acoustic sensing (DAS), distributed temperature sensing (DTS) fiber-optic, completions, flow scanner production log, and surface data to model daily gas production from a 28-stage stimulated horizontal well drilled in the Marcellus Shale of the Appalachian basin in North America. In addition, this study aims to utilize the data from a fiber-optic monitoring system, such as DAS and DTS to evaluate the well performance. We build supervised data-driven machine learning models using Random Forest (RF), Artificial Neural Network (ANN), and Support Vector Machine (SVM) algorithms to understand the well performance and forecast the daily gas production. The study compares the forecasted results, tests the level of accuracy, and addresses different issues with these machine learning algorithms. A spatio-temporal database is constructed and used to perform sensitivity analyses to identify the key drivers for gas production. The results show that RF and ANN algorithms can be used to predict daily gas production with significantly high accuracy; however, RF algorithm is the best predictor in terms of highest accuracy (96%), less computational time and cost. Based on sensitivity analysis, it appears that Poisson's ratio, minimum horizontal stress, DAS, casing pressure, gamma log are the most important parameters to predict gas production. Highlights • Application of machine learning algorithms to forecast daily gas production from unconventional reservoirs using large multi-sensor dataset. • Random Forest and Artificial Neural Network can model daily gas production from each stage efficiently. [ABSTRACT FROM AUTHOR]

Upscaling of fracture permeability in a field-size discrete fracture network (DFN) for a dual porosity model can be undertaken using Oda's method. It is known that Oda's method usually overestimates equivalent permeability and needs to be corrected by a flow-based numerical method. In this study we show that grid orientation can produce significant differences in single-phase (water) production volumes estimated through flow simulation in dual porosity models (Eclipse 300) when using corrected Oda's method. A five layer synthetic model in a water zone includes thirteen vertical wells. 10 ft by 10 ft structured gridding is performed in six directions: 0°, 15°, 30°, 45°, 60° and 75° counter-clockwise relative to the north. The model DFN used in this study consists of three sets: a dominant, high intensity N76°W set and two lower-intensity sets that strike N28°W and N75°E similar to the fracture sets observed in the Tensleep Formation at Teapot Dome, Wyoming, USA. The single-phase (water) flow simulation using Eclipse 300 flow simulator for the six synthetic models reveals that the production varies in proportion to the geometric mean of the permeability tensor with grid rotation. The production is at its maximum when the dominant fracture set orients 45° relative to the grid cell principal directions ( i , j ) and falls to a minimum when the dominant fracture set is parallel to either grid wall ( i or j principal directions). This research reveals a shortcoming of systematic variability of simulated production with grid orientation even though Oda's results are corrected with a flow-based numerical method. [ABSTRACT FROM AUTHOR]

Abstract Porosity and pore size distribution (PSD) are critical reservoir parameters. Pore surface area, pore volume, PSD, and porosity were measured using subcritical nitrogen (N 2) adsorption, and helium porosimetry. A suite of 17 samples were collected from 4 wells in Pennsylvania and West Virginia to analyze the evolution of porosity with increasing thermal maturity in Middle Devonian shales of the Appalachian Basin. The thermal maturity of the tested samples covers a wide range in the hydrocarbon generation sequence from wet gas/condensate zone (vitrinite reflectance (R o) = 1.16%) to post-mature zone (R o = 2.79%). Shale samples from the Marcellus Shale and Mahantango Formation used in this study have total organic carbon contents from 0.41 to 7.88 wt%. Results indicate that total organic carbon (TOC) has the strongest effect on porosity and pore structure. The presence of organic matter in shale strongly enhances the storage capacity by increasing the specific surface area and pore volume, which represents sorption storage capacity and free-gas storage capacity. Differences in porosity and pore structure have a complex relationship to thermal maturity, micro texture, mineralogy, clay content, and TOC. [ABSTRACT FROM AUTHOR]