Hydraulic Fracture Treatment and Landing Zone Interval Optimization: An Eagle Ford Case Study

This paper focuses on optimizing future well landing zones and their corresponding hydraulic fracture treatments in the Eagle Ford shale play. The optimum landing zone and stimulation treatment were determined by analyzing multiple landing zone options, including the lower Austin Chalk, Eagle Ford, and Pepper Shale, with several hydraulic fracturing treatment possibilities. Fracturing fluids and their volume, proppant size, and cluster spacing were investigated to determine the optimum hydraulic fracturing treatment for the subject geologic area. Ranges of 75,000 to 300,000 gallons of pure gel, pure slickwater, and hybrid fracturing fluids along with 20/40, 30/50, 40/70, and 100 mesh proppant were tested. Cluster spacing of twenty feet to eighty feet were also sensitized in this study. A fully three-dimensional hydraulic fracture modeling software was used to develop a geological and geomechanical model of the studied area. The generated model was calibrated with available field data to ensure that the model reflects the area's geological and geomechanical characteristics. The developed model was used to create fracture results for each sensitized parameter. Production analysis was performed for all fracture models to determine the optimum landing zone and fracturing treatment implications. The study shows that the Eagle Ford had better production than the lower Austin Chalk in the subject area. The Pepper Shale had the highest potential hydrocarbon production, around 326 Mbbl cumulative, when fractured with a pure gel treatment. The analyses showed that a hybrid treatment with 70% gel and 30% slickwater yielded the optimum production due to the treatment economics even though the highest production was obtained using the pure gel. Treating the formation with larger proppant provided better production than smaller proppant due to conductivity concerns associated with damaging mechanisms in the studied area. Since increasing the volume above 175,000 gallons caused a negligible increase in the production, 175,000 gallons of fracturing fluid per stage appeared to be the optimum fracturing fluid volume. Thirty-foot cluster spacing was the optimum spacing in the study area. Overall, the study suggests that oil production can be improved in the Eagle Ford study area through a detailed workflow development and optimization process. The hydraulic fracture treatment and landing zone optimization workflow ensures optimum hydrocarbon extraction from the study area. The developed workflow can be applied to new unconventional plays instead of using trial and error methods.

RESERVOIR SCALE CHEMOSTRATIGRAPHY AND FACIES MODELING USING HIGH SAMPLE RATE GEOPHYSICAL SCANS OF WHOLE CORE

Many unconventional reservoirs exhibit a high level of vertical heterogeneity in terms of petrophysical and geo-mechanical properties. These properties often change on the scale of centimeters across rock types or bedding, and thus cannot be accurately measured by low-resolution petrophysical logs. Nonetheless, the distribution of these properties within a flow unit can significantly impact targeting, stimulation and production. In unconventional resource plays such as the Austin Chalk and Eagle Ford shale in south Texas, ash layers are the primary source of vertical heterogeneity throughout the reservoir. The ash layers tend to vary considerably in distribution, thickness and composition, but generally have the potential to significantly impact the economic recovery of hydrocarbons by closure of hydraulic fracture conduits via viscous creep and pinch-off. The identification and characterization of ash layers can be a time-consuming process that leads to wide variations in the interpretations that are made with regard to their presence and potential impact. We seek to use machine learning (ML) techniques to facilitate rapid and more consistent identification of ash layers and other pertinent geologic lithofacies. This paper involves high-resolution laboratory measurements of geophysical properties over whole core and analysis of such data using machine-learning techniques to build novel high-resolution facies models that can be used to make statistically meaningful predictions of facies characteristics in proximally remote wells where core or other physical is not available. Multiple core wells in the Austin Chalk/Eagle Ford shale play in Dimmitt County, Texas, USA were evaluated. Drill core was scanned at high sample rates (1 mm to 1 inch) using specialized equipment to acquire continuous high resolution petrophysical logs and the general modeling workflow involved pre-processing of high frequency sample rate data and classification training using feature selection and hyperparameter estimation. Evaluation of the resulting training classifiers using Receiver Operating Characteristics (ROC) determined that the blind test ROC result for ash layers was lower than those of the better constrained carbonate and high organic mudstone/wackestone data sets. From this it can be concluded that additional consideration must be given to the set of variables that govern the petrophysical and mechanical properties of ash layers prior to developing it as a classifier. Variability among ash layers is controlled by geologic factors that essentially change their compositional makeup, and consequently, their fundamental rock properties. As such, some proportion of them are likely to be misidentified as high clay mudstone/wackestone classifiers. Further refinement of such ash layer compositional variables is expected to improve ROC results for ash layers significantly.