Application of Statistical Methods to Predict Production From Liquid-Rich Shale Reservoirs
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2017, Unconventional Resources Technology Conference (URTeC). With the advance of technology in multi-fractured horizontal wells, production from shale plays has improved significantly, making them become viable sources of oil and gas production. While these unconventional resources bring great benefits to the industry, production prediction from these wells has proven to be challenging. In this paper, our goal is to present a statistical method for the prediction of production from liquid-rich shale and other ultra-low permeability reservoirs. This method starts from the learning process from multiple wells with sufficiently long production histories. Functional principal component analysis (FPCA) is applied to extract key features of production patterns. Then, principal component functions obtained from the training production data set are used as the basis to construct a linear production model from which we can predict production from other wells. Multiple test wells are selected for validation to compare predicted results to true production data. The approach in this paper is driven by production data and it has several advantages over empirical models used in decline curve analysis. Production forecasting has significant consequences in investment decision making and is also a major component of reserves estimation required for reports to regulatory agencies. With the aid of the approaches proposed in this work, we can improve reserves estimation, field management and evaluation of project economics.
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Proceedings of the 5th Unconventional Resources Technology Conference
