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Summary Researchers from both industry and academia have intensively studied tight oil resources in the past decade since the successful development of Bakken Shale and Eagle Ford Shale, and have made tremendous progress. It has been recognized that locating the sweet spots in the regionally pervasive plays is of great significance. However, we are still struggling to determine whether the dominant control on shale-well productivity is geologic or technical. Given certain geological properties, what is the best completion strategy? Most of the previous studies either analyze the completion data alone or divide the entire play into different data clusters by map coordinates and depth, which might neglect the heterogeneity in thickness and reservoir-quality parameters. In our study, we first conducted stratigraphic and petrophysical analyses, using the regional variation in depth, thickness, porosity, and water saturation to capture the regional heterogeneity in the Bakken Shale petroleum system. We selected approximately 2,000 horizontal wells, targeting the Middle Bakken Formation with detailed completion records and initial production dates during 2013 and 2014. Completion data inputs include normalized stage length (NSL), stage counts, normalized volume of fluid (NVF), and normalized volume of proppant (NVP). We investigated the relationship between the geological and completion features, and its effect on the first year of production. Then, we built a neural-network model to identify the relationship between the first-year oil production and the selected features. We separated the data into three sets for training, validation, and testing. After we trained the model using the training and validation set, we tested the model to estimate its robustness. Through sensitivity analysis, we demonstrated how the completion parameters combined with geological input would affect the production. The developed technique provides a method to identify the best well location, understand the effectiveness of the completion strategy, and predict the well production. Although the data used came from wells in the Bakken Shale, the methodology applies in a similar way to other tight oil plays.

Drilling through naturally fractured formations results in multiple complications; fracture ballooning and loss circulation are predominant among them that requires more understanding of the process. In order to better understand the mechanism behind fluid loss into the natural fractures a new model has been developed which addresses the shortcomings of pre-existing models. The paper presents a new comprehensive analytical model; represented by partial differential equation that can be used for various fluid rheologies and thus, eliminating the need for different models for different fluids. The model also takes into consideration of fracture deformation with constant and variable leak off rates into the formation. The proposed model was validated by reducing it to the preexisting models by incorporating their assumptions into it. The model has also been validated using synthetic as well as multiple field data. Sensitivity analyses were performed to determine the effects of fracture normal stiffness, leak off rate, mud-yield stress, aperture and other parameters on fluid losses. The new model presented demonstrates that fracture deformation and leak off pressure play important roles in the determination of fluid loss into the formation. The results have shown that by neglecting the fracture deformation or leak off results in the under estimation of fluid losses. It has also been found that the fluid Yield stress plays an important role in preventing fluid loss into the formation. Sensitivity analysis shows that once the pressure potential falls below the yield stress value, the fluid migration into the fracture is stopped. The novelty of the proposed model lies in the fact that it can be used for different drilling fluids and also more generic. It takes into account fracture deformation and leak-off into the formation which lead to more accurate estimation of fluid losses than the existing models.

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