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Degenhardt, John J. (W. D. Von Gonten Laboratories) | Ali, Safdar (W. D. Von Gonten Laboratories) | Ali, Mansoor (W. D. Von Gonten Laboratories) | Chin, Brian (W. D. Von Gonten Laboratories) | Von Gonten, W. D. (W. D. Von Gonten Laboratories) | Peavey, Eric (Shell Fellow-UROC / Texas A&M University)
Abstract 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.
Suarez-Rivera, Roberto (W. D. Von Gonten Laboratories) | Panse, Rohit (W. D. Von Gonten Laboratories) | Sovizi, Javad (Baker Hughes) | Dontsov, Egor (ResFrac Corporation) | LaReau, Heather (BP America Production Company, BPx Energy Inc.) | Suter, Kirke (BP America Production Company, BPx Energy Inc.) | Blose, Matthew (BP America Production Company, BPx Energy Inc.) | Hailu, Thomas (BP America Production Company, BPx Energy Inc.) | Koontz, Kyle (BP America Production Company, BPx Energy Inc.)
Abstract Predicting fracture behavior is important for well placement design and for optimizing multi-well development production. This requires the use of fracturing models that are calibrated to represent field measurements. However, because hydraulic fracture models include complex physics and uncertainties and have many variables defining these, the problem of calibrating modeling results with field responses is ill-posed. There are more model variables than can be changed than field observations to constrain these. It is always possible to find a calibrated model that reproduces the field data. However, the model is not unique and multiple matching solutions exist. The objective and scope of this work is to define a workflow for constraining these solutions and obtaining a more representative model for forecasting and optimization. We used field data from a multi-pad project in the Delaware play, with actual pump schedules, frac sequence, and time delays as used in the field, for all stages and all wells. We constructed a hydraulic fracturing model using high-confidence rock properties data and calibrated the model to field stimulation treatment data varying the two model variables with highest uncertainty: tectonic strain and average leak-off coefficient, while keeping all other model variables fixed. By reducing the number of adjusting model variables for calibration, we significantly lower the potential for over-fitting. Using an ultra-fast hydraulic fracturing simulator, we solved a global optimization problem to minimize the mismatch between the ISIPs and treatment pressures measured in the field and simulated by the model, for all the stages and all wells. This workflow helps us match the dominant ISIP trends in the field data and delivers higher confidence predictions in the regional stress. However, the uncertainty in the fracture geometry is still large. We also compared these results with traditional workflows that rely on selecting representative stages for calibration to field data. Results show that our workflow defines a better global optimum that best represents the behavior of all stages on all wells, and allows us to provide higher-confidence predictions of fracturing results for subsequent pads. We then used this higher confidence model to conduct sensitivity analysis for improving the well placement in subsequent pads and compared the results of the model predictions with the actual pad results.
Dontsov, Egor (ResFrac Corporation) | Suarez-Rivera, Roberto (W. D. Von Gonten Laboratories) | Panse, Rohit (W. D. Von Gonten Laboratories) | Quinn, Christopher (W. D. Von Gonten Laboratories) | LaReau, Heather (BP America Production Company, BPx Energy Inc.) | Suter, Kirke (BP America Production Company, BPx Energy Inc.) | Hines, Chris (BP America Production Company, BPx Energy Inc.) | Montgomery, Ryan (BP America Production Company, BPx Energy Inc.) | Koontz, Kyle (BP America Production Company, BPx Energy Inc.)
Abstract As the number of wells drilled in regions with existing producing wells increases, understanding the detrimental impact of these by the depleted zone around parent wells becomes more urgent and important. This understanding should include being able to predict the extent and heterogeneity of the depleted region near the pre-existing wells, the resulting altered stress field, and the effect of this on newly created fractures from adjacent child wells. In this paper we present a workflow that addresses the above concern in the Eagle Ford shale play, using numerical simulations of fracturing and reservoir flow, to define the effect of the depletion zone on child wells and match their field production data. We utilize an ultra-fast hydraulic fracture and depletion model to conduct several hundred numerical simulations, with varying values of permeability and surface area, seeking for cases that match the field production data. Multiple solutions exist that match the field data equally well, and we used additional field production data of parent-child well-interaction, to select the most plausible model. Results show that the depletion zone is strongly non-uniform and that large reservoir regions remain undepleted. We observe two important effects of the depleted zone on fractures from child wells drilled adjacent to the parents. Some fractures propagate towards low pressure zones and do not contribute to production. Others are repelled by the higher stress region that develops around the depletion zone, propagate into undepleted rock, and have production rates commensurate to that from other child wells drilled away from depleted region. The observations are validated by the field data. Results are being used to optimize well placement and well spacing for subsequent field operations, with the objective to increase the effectiveness of the child wells.
Summary Multiple hydraulic fractures are often generated simultaneously from a wellbore to increase efficiency of reservoir stimulation. Numerical modeling of such a system of fractures is computationally costly, especially if the goal is to simulate numerous stages, each containing multiple fractures, on different wells, which is the current trend in the petroleum industry. To address the challenge, this study defines a method and a workflow to represent the simultaneous propagation of multiple fractures with a reduced number of equivalent fractures that accurately describes the overall fracture geometry, the created surface area, the propped surface area, the fluid leakoff, and the resulting induced stresses, as resulting from the original configuration. A hybrid approach is used, in which a combination of physical modeling and data science is involved. We first develop a database of numerical solutions using a fully coupled hydraulic fracturing simulator. The equivalent fracture representation is quantified for each set of problem parameters presented in the database. Then, the results of the database solutions are used to tackle more general cases with field pumping schedules and rock properties. Several numerical examples are presented to validate and illustrate the developed concept.