Asset Evaluation Utilizing Multi-Variate Statistics Integrating Data-Mining, Completion Optimization, and Geology Focused on Multi-Bench Shale Plays

Abstract

The objective is to establish a robust methodology for evaluating the true economic potential of an asset integrating various sources of data, namely, geological, completions, production and leasing data retrieved from public sources. A three segment methodology has been adopted in assessing properties for acquisition or farmout opportunities. The central idea of the work is that completions optimization is an ever-changing process. This implies the areas that were previously overlooked could potentially benefit from recent advances in completions and be more profitable. Data mining integrated with available geological information assists in identifying the key parameters that affect well performance. Once optimized for completions parameters, one can identify the real potential of an asset under consideration. When geological factors, completions parameters, normalized production type curves, cost of drilling, completions and leasing are taken into account, we reach an even ground comparing different assets. Using this methodology, opportunities were identified in different areas that were previously overlooked. Some of the geological factors that should always be taken into account are depth (TVD), reservoir pressure, existing PVT/GOR studies, and prior knowledge of faults/fractures. The completions parameters that influence production have a wide range. However, it is reasonable to assume that length of lateral, stage spacing, type of fluid, proppant amount, rate of pumping and clusters per stage are the more important ones. The proppant amount, fluid type and rate of pumping can be inter-dependent. In areas with a significant number of horizontal wells on production, data on completions, geology and production is usually available on Public Sources, like the Texas Railroad Commission. The aggregation of data on several wells feeds into the creation of a huge data-set, in other words, big data. Once a combination of above mentioned factors is taken into account, the pattern identification process related to correlation of performance vs. parameters is quickly established. The type curve which corresponds to the optimum parameters for the particular area of interest, is then used for economic forecasting. This paper truly integrates various factors that go into investment decision making. The advances in data visualization tools and the availability of big data from public sources assist in solidifying this robust methodology. The methodology described can be utilized by E&P companies, investment banks and private equity groups to make well informed investment decisions in the future.