Download or read book Production Analysis and Forecasting of Shale Reservoirs Using Simple Mechanistic and Statistical Modeling written by Leopoldo Matias Ruiz Maraggi and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Accurate production analysis and forecasting of well’s performance is essential to estimate reserves and to develop strategies to optimize hydrocarbon recovery. In the case of shale resources, this task is particularly challenging for the following reasons. First, these reservoirs show long periods of transient linear flow in which the reservoir volume grows continuously over time acting without bounds. Second, variable operating conditions cause scatter and abrupt production changes. Finally, the presence of competing flow mechanisms, heterogeneities, and multi-phase flow effects make the production analysis more complex. Detailed numerical flow models can address the complexities present in unconventional reservoirs. However, these models suffer from the following limitations: (a) the uncertainty of many input parameters, (b) susceptibility to overfit the data, (c) lack of interpretability of their results, and (d) high computational expense. This dissertation provides new and simple mechanistic and statistical modeling tools suitable to improve the production analysis and forecasts of shale reservoirs. This work presents solutions to the following research problems. This study develops and applies a new two-phase (oil and gas) flow suitable to history-match and forecast production of tight-oil and gas-condensate reservoirs producing below bubble- and dew-point conditions, respectively. It solves flow equations in dimensionless form and uses only two scaling parameters (hydrocarbon in-place and characteristic time) to history-match production. For this reason, the model requires minimal time to run making it ideal for decline curve analysis on large numbers of wells. This research illustrates the development and application of a Bayesian framework that generates probabilistic production history matches and forecasts to address the uncertainty of model’s estimates. This work uses an adaptative Metropolis-Hastings Markov chain Monte Carlo (MCMC) algorithm to guarantee a fast convergence of the Markov chains by accounting for the correlation among model’s parameters. In addition, this study calibrates the model’s probabilistic estimates using hindcasting and evaluates the inferences robustness using posterior predictive checks. This dissertation examines the problem of evaluation, ranking and selection, and averaging of models for improved probabilistic production history-matching and forecasting. We illustrate the assessment of the predictive accuracy of four rate-time models using the expected log predictive density (elpd) accuracy metric along with cross-validation techniques (leave-one-out and leave-future-out). The elpd metric provides a measure of out-of-sample predictive accuracy of a model’s posterior distribution. The application of Pareto smoothed importance sampling (PSIS) allows to use cross-validation techniques without the need of refitting Bayesian models. Using the Bayesian Bootstrap, this work generates a model ensemble that weighs each individual model based on the accuracy of its predictions. Finally, this research applies a novel deconvolution technique to incorporate changing operating conditions into rate-time analysis of tight-oil and shale gas reservoirs. Furthermore, this work quantifies the errors and discusses the limitations of the standard rate-transient analysis technique used in production analysis of unconventional reservoirs: rate normalization and material balance time