This thesis gives us a better understanding of the behavior of shale volatile oil reservoirs. The effects of fluid compositions as well as the sensitivity of certain variables on cumulative oil production and rates were analyzed using black-oil and compositional simulations. Two-phase (oil and gas) black-oil simulations gave better results than single-phase (oil) black-oil simulations. Compositional simulations were much better in comparison to two-phase black-oil simulations. Therefore, for thorough analysis of fluid composition effects and more accurate production forecasts (especially for reservoir fluids like volatile oils in shale formations), compositional simulations are necessary. In this research, single-phase and two-phase black-oil simulations were run on a base case model and the results were compared. Sensitivity studies were carried out by varying certain parameters in the base case model, then single-phase and two-phase black-oil simulations were run and the results were compared to the base case model. This was followed by analyzing six different fluid samples through compositional simulations. Flash calculations were later done on the fluid samples to obtain inputs for two-phase black-oil simulations. Finally, the simulation results from the compositional and two-phase black-oil simulations were then compared. The importance of shale oil and gas research cannot be over-emphasized, given the ever-rising global demand for energy. Research and studies like this, can lead to better well completions and design, improve reservoir management and economics as well as provide insight into potential alternative methods to enhance recovery from unconventional shale formations.

With the demand for oil rising, unconventional oil reservoirs have taken a prominent role in the United States as a source of crude oil. Different methodologies to estimate reserves for shale gas and coal bed methane have, thus far, proved to be reliable, but no simple yet accurate workflow has been generally accepted to forecast production and estimate reserves for shale oil. To fill this gap in technology, we proposed and validated a workflow that integrates analytical methods with empirical methods. The final methodology is both easily applied and accurate. In developing the final workflow, we evaluated several alternatives, most of which proved to be unsuitable. We also investigated the use of a filter to eliminate outliers in a systematic way, as proposed by Rastogi (2014). The workflow was successfully applied to three of four volatile oil wells in the Eagle Ford shale, with similar results. The analytical model that best matched the wells is called the Stimulated Reservoir Volume (SRV) Bounded Model by the software marketer Kappa. We tested this and other models using a Beta test version of new Kappa software. While accurate, this modeling approach is too time consuming for routine use. We found that a simple empirical approach that led to the same results as the analytical model was a 3-segment Arps decline model. The early flow regime was transient linear for all the wells; thus an Arps --b‖ parameter of two was appropriate. When boundary-influenced flow (BIF) appeared later, b-values of 0.2 were found appropriate. The initial decline rate (Di) value during BIF was modified in mid-segment leading to a distinct third segment. Our workflow also led to reliable forecasts of production (to date) of the gas-oil ratio for the three wells.

The massive increase in energy demand and the related rapid development of unconventional reservoirs has opened up exciting new energy supply opportunities along with new, seemingly intractable engineering and research challenges. The energy industry has primarily depended on a heuristic approach—rather than a systematic approach—to optimize and tackle the various challenges when developing new and improving the performance of existing unconventional reservoirs. Industry needs accurate estimations of well production performance and of the cumulative estimated ultimate reserves, accounting for uncertainty. This Special Issue presents 10 original and high-quality research articles related to the modeling of unconventional reservoirs, which showcase advanced methods for fractured reservoir simulation, and improved production forecasting techniques.

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

Different methods have been proposed for history matching production of shale gas/oil wells which are drilled horizontally and usually hydraulically fractured with multiple stages. These methods are simulation, analytical models, and empirical equations. It has been well known that among the methods listed above, analytical models are more favorable in application to field data for two reasons. First, analytical solutions are faster than simulation, and second, they are more rigorous than empirical equations. Production behavior of horizontally drilled shale gas/oil wells has never been completely matched with the models which are described in this thesis. For shale gas wells, correction due to adsorption is explained with derived equations. The algorithm which is used for history matching and forecasting is explained in detail with a computer program as an implementation of it that is written in Excel's VBA. As an objective of this research, robust method is presented with a computer program which is applied to field data. The method presented in this thesis is applied to analyze the production performance of gas wells from Barnett, Woodford, and Fayetteville shales. It is shown that the method works well to understand reservoir description and predict future performance of shale gas wells. Moreover, synthetic shale oil well also was used to validate application of the method to oil wells. Given the huge unconventional resource potential and increasing energy demand in the world, the method described in this thesis will be the "game changing" technology to understand the reservoir properties and make future predictions in short period of time.

This thesis provides a new methodology to forecast ultimate recovery, based on more reliable production forecast for shale oil wells using historical production data. Compared to available decline curve methods including Arps (AIME: 160, 228-247), Valko (SPE 134231) and Duong (SPE 137748), this method is more accurate and more conservative. Production forecasts play a vital role in determining the value of oil or gas wells, and improved accuracy enhances management decisions on field development. The new, more accurate method was verified using both field data and numerical simulations. This method can potentially be used in most shale reservoirs producing single-phase liquid.

Shale Gas and Tight Oil Reservoir Simulation delivers the latest research and applications used to better manage and interpret simulating production from shale gas and tight oil reservoirs. Starting with basic fundamentals, the book then includes real field data that will not only generate reliable reserve estimation, but also predict the effective range of reservoir and fracture properties through multiple history matching solutions. Also included are new insights into the numerical modelling of CO2 injection for enhanced oil recovery in tight oil reservoirs. This information is critical for a better understanding of the impacts of key reservoir properties and complex fractures. Models the well performance of shale gas and tight oil reservoirs with complex fracture geometries Teaches how to perform sensitivity studies, history matching, production forecasts, and economic optimization for shale-gas and tight-oil reservoirs Helps readers investigate data mining techniques, including the introduction of nonparametric smoothing models