Black shale lithofacies prediction and distribution Pattern analysis of middle Devonian Marcellus Shale in the Appalachian Basin, northeastern U.S.A.

The Marcellus Shale, marine organic-rich mudrock deposited during Middle Devonian in the Appalachian basin, is considered the largest unconventional shale-gas resource in United State. Although homogeneous in the appearance, the mudstone shows heterogeneity in mineral composition, organic matter richness, gas content, and fracture density. Two critical factors for unconventional mudstone reservoirs are units amenable to hydraulic fracture stimulation and rich of organic matter. The effectiveness of hydraulic fracture stimulation is influenced by rock geomechanical properties, which are related to rock mineralogy. The natural gas content in mudrock reservoirs has a strong relationship with organic matter, which is measured by total organic carbon (TOC). In place of using petrographic information and sedimentary structures, Marcellus Shale lithofacies were based on mineral composition and organic matter richness and were predicted by conventional logs to make the lithofacies \u27meaningful’, ‘predictable’ and ‘mappable’ at multiple scales from the well bore to basin. Core X-ray diffraction (XRD) and TOC data was used to classify Marcellus Shale into seven lithofacies according to three criteria: clay volume, the ratio of quartz to carbonate, and TOC. Pulsed neutron spectroscopy (PNS) logs provide similar mineral concentration and TOC content, and were used to classify shale lithofacies by the same three criteria. Artificial neural network (ANN) with improvements (i.e., learning algorithms, performance function and topology design) was utilized to predict Marcellus Shale lithofacies in 707 wells with conventional logs. To improve the effectiveness of wireline logs to predict lithofacies, the effects of barite and pyrite were partly removed and eight petrophysical parameters commonly used for a conventional reservoir analysis were derived from conventional logs by petrophysical analysis. These parameters were used as input to the ANN analysis. Geostatistical analysis was used to develop the experimental variogram models and vertical proportion of each lithofacies. Indictor kriging, truncated Gaussian simulation (TGS), and sequential indicator simulation (SIS) were compared, and SIS algorithm performed well for modeling Marcellus Shale lithofacies in three-dimensions. Controlled primarily by sediment dilution, organic matter productivity, and organic matter preservation/decomposition, Marcellus Shale lithofacies distribution was dominantly affected by the water depth and the distance to shoreline. The Marcellus Shale lithofacies with the greatest organic content and highest measure of brittleness is concentrated along a crescent shape region paralleling the inferred shelf and shoreline, showing shape of crescent paralleling with shoreline. The normalized average gas production rate from horizontal wells supported the proposed approach to modeling Marcellus Shale lithofacies. The proposed 3-D modeling approach may be helpful for (1) investigating the distribution of each lithofacies at a basin-scale; (2) developing a better understanding of the factors controlling the deposition and preservation of organic matter and the depositional model of marine organic-rich mudrock; (3) identifying organic-rich units and areas and brittle units and areas in shale-gas reservoirs; (4) assisting in the design of horizontal drilling trajectories and location of stimulation activity; and (5) providing input parameters for the simulation of gas flow and production in mudrock (e.g., porosity, permeability and fractures)

Storage Capacity Estimation of Commercial Scale Injection and Storage of CO2 in the Jacksonburg-Stringtown Oil Field, West Virginia

Geological capture, utilization and storage (CCUS) of carbon dioxide (CO2) in depleted oil and gas reservoirs is one method to reduce greenhouse gas emissions with enhanced oil recovery (EOR) and extending the life of the field. Therefore CCUS coupled with EOR is considered to be an economic approach to demonstration of commercial-scale injection and storage of anthropogenic CO2. Several critical issues should be taken into account prior to injecting large volumes of CO2, such as storage capacity, project duration and long-term containment. Reservoir characterization and 3D geological modeling are the best way to estimate the theoretical CO 2 storage capacity in mature oil fields. The Jacksonburg-Stringtown field, located in northwestern West Virginia, has produced over 22 million barrels of oil (MMBO) since 1895. The sandstone of the Late Devonian Gordon Stray is the primary reservoir.;The Upper Devonian fluvial sandstone reservoirs in Jacksonburg-Stringtown oil field, which has produced over 22 million barrels of oil since 1895, are an ideal candidate for CO2 sequestration coupled with EOR. Supercritical depth (\u3e2500 ft.), minimum miscible pressure (941 psi), favorable API gravity (46.5°) and good water flood response are indicators that facilitate CO 2-EOR operations. Moreover, Jacksonburg-Stringtown oil field is adjacent to a large concentration of CO2 sources located along the Ohio River that could potentially supply enough CO2 for sequestration and EOR without constructing new pipeline facilities.;Permeability evaluation is a critical parameter to understand the subsurface fluid flow and reservoir management for primary and enhanced hydrocarbon recovery and efficient carbon storage. In this study, a rapid, robust and cost-effective artificial neural network (ANN) model is constructed to predict permeability using the model\u27s strong ability to recognize the possible interrelationships between input and output variables. Two commonly available conventional well logs, gamma ray and bulk density, and three logs derived variables, the slope of GR, the slope of bulk density and Vsh were selected as input parameters and permeability was selected as desired output parameter to train and test an artificial neural network. The results indicate that the ANN model can be applied effectively in permeability prediction.;Porosity is another fundamental property that characterizes the storage capability of fluid and gas bearing formations in a reservoir. In this study, a support vector machine (SVM) with mixed kernels function (MKF) is utilized to construct the relationship between limited conventional well log suites and sparse core data. The input parameters for SVM model consist of core porosity values and the same log suite as ANN\u27s input parameters, and porosity is the desired output. Compared with results from the SVM model with a single kernel function, mixed kernel function based SVM model provide more accurate porosity prediction values.;Base on the well log analysis, four reservoir subunits within a marine-dominated estuarine depositional system are defined: barrier sand, central bay shale, tidal channels and fluvial channel subunits. A 3-D geological model, which is used to estimate theoretical CO2 sequestration capacity, is constructed with the integration of core data, wireline log data and geological background knowledge. Depending on the proposed 3-D geological model, the best regions for coupled CCUS-EOR are located in southern portions of the field, and the estimated CO2 theoretical storage capacity for Jacksonburg-Stringtown oil field vary between 24 to 383 million metric tons. The estimation results of CO2 sequestration and EOR potential indicate that the Jacksonburg-Stringtown oilfield has significant potential for CO2 storage and value-added EOR