In recent years, liquid rich shale plays have become the focus of many operators developing unconventional resources. After years of drilling single wells in a section to hold leases, companies are facing key field development questions – what is the appropriate well spacing and what is the right development plan for their unconventional assets? Some operators have simply used a trial and error approach while others have designed and drilled pilots to test various well spacing scenarios and multiple field development strategies following an experimental design approach. Both of these methods could take significant time before conclusive results could be observed, and these field trials often involve significant capital investment to test a few combinations with the hope of inferring the optimal solution. Another key question many operators have is how does the optimal well spacing change with fluid type or with shale play? Liquid rich shale plays like Eagle Ford, Utica and Duvernay show multiple fluid gradients. Depending on which fluids a company's acreage contains, the optimal well spacing will vary as will the development plans. Therefore, it is very important to understand the effect of fluid composition on recovery and well spacing in liquid rich shale plays. In this work, a reservoir flow simulation model is built and history matched to a synthetic liquid rich shale well production history in order to predict the estimated ultimate recovery (EUR) for various well spacing scenarios. The flow simulation model incorporates dominant shale flow and storage mechanisms including multi-phase physics. Using the calibrated simulation model, a study was performed to quantify the reduction in EUR due to well interference and sensitivities to fluid composition were conducted to understand its effects on well productivity and recovery factor. Results indicate that the outlined workflow provides a reliable tool for performance evaluation of various field development strategies and that well performance and fluid composition can have a significant impact on well spacing and field development decisions.

Shale plays have been considered statistical plays by many people in our industry. During the early years of the shale boom and even today, technical teams have looked to find correlations between variables to help explain well performance. Simple one-to-one correlations between individual variables and production performance in the Haynesville Shale appear to be weak because complex relationships may exist. Regression analysis techniques have assumptions and limitations, moreover, such techniques encounter difficulties when analyzing complex relationships. In this study, ordination, a multivariate analysis technique, was applied to subsurface and completion data to identify the variables that seem to influence well performance. The debate of nature versus nurture has often been applied to shale gas developments where some believe an effective well completion can make up for a poor shale reservoir rock quality while others believe a high quality shale reservoir rock is essential to obtain better producers despite an average well completion. The ordination technique successfully separated better performing wells from the less prolific wells and identified the variables that characterize the better producers. The same technique provided various optimum ranges for the different variables that could be used to identify core and non-core areas within the play. Our results suggest that (1) ordination is a viable method for multivariate statistical analysis in the Haynesville Shale; (2) subsurface variables are critical in obtaining better performing wells; and (3) completions variables are secondary but also very important.

This paper showcases how public data was integrated using data visualization software to develop appropriate analog information for new prospects and field evaluations in Brazilian Campos and Santos and Mexican Gulf of Mexico deepwater plays. The data visualization software was selected for its ability to integrate geologic map files (.shp files) and digital production data. This work was extended to evaluate Mexican opportunities since similar production data is available from the government. In the exploration workflow, analog field data is used to develop potential prospect estimated ultimate recovery ("EUR"), recovery factors, the appropriate EUR per well and initial production rates. This information is then used in a conceptual development plan that allows potential economic scenarios to be assessed. Both in Brazil and Mexico, substantial well data is available from the respective governments. The team successfully used this information to develop appropriate low, mid, and high oil production forecasts.