As major oil and gas companies have been investing in greener energy resources, even though has been part of the oil and gas industry for long time, it has gained its popularity back. Oil and gas industry has adapted to the wind of change and has started investing in and utilizing the technologies significantly. In this perspective, this study investigates and outlines the latest advances, technologies in artificial lift methods as it is applied to unconventionals in a comprehensive manner to serve as an industry-reference for petroleum industry. In order to optimize production of oil and gas from unconventional wells, artificial lift (AL) is found to be the most effective method of production. However, as the well passes through its production lifespan, the reservoir pressure declines and artificial lifting of hydrocarbon fluids remains a serious problem. Identifying the best approach, the kind of AL, ideal time and settings to mount the AL as the well passes through its production lifespan is a major challenge. Several investigations have been carried out in the past to identify and establish ground-breaking techniques to improve the application of AL in tight reservoirs. The aim of this study is to conduct a comprehensive review of recent developments in the field of AL and its applications. A review of the application of machine learning (ML) is also presented in this study. A comprehensive literature review focusing on the recent developments and findings in artificial lift along with the availability and locations are outlined and discussed under the current dynamics of the oil and gas developments. Literature review includes a broad spectrum that spans from technical petroleum literature with very comprehensive research using onepetro and other databases to other renowned resources including journals and other publications. The information and data are summarized and outlined. This study also provides the techniques on optimziation of artificial lift methods in addition to their comprehensive details with a comparative approach. Aritifical lift is a critical component of any oil and gas well especially in unconventionals. The key artificial lift selection and optimziation is through a robust design and established reservoir and well management practices and reflecting the production physics and well dynamics in all aspects. This study outlines the key criteria in the success of aritifical lift applications in individual examples using different methods that will serve for the future decisions as a comprehensive and collective review of all the aspects of the employed techniques and their usability in specific cases. Among the few existing studies that explain the methods of aritficial lift are up-to-date and the existing studies within SPE domain focus on certain methods. This study closes the gap and aims to serve as a comprehensive single-source reference for artificial lift and its optimization in unconventionals including all traditional and relatively-new methods.

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Reserve/Resource estimation plays a crucial role in a feasible oil and gas business. Tendency of producing more from unconventional reservoirs and their relatively new as well as different structure brought on a learning curve for the applications of new reservoir evaluation methods. This study clearly desribes all reserve and resource evaluation techniques, the latest developments in this area by providing a single-source up-to-date reference for reserves evaluation in unconventionals. An extensive review of literature has been applied to describe all available reserve evaluation techniques and their utilization, applicability and robustness, the history of using these techniques, types of technologies which applied in conventional reservoirs and transferred to unconventionals, and their incremental benefits of usage.This paper includes a real worldwide case studies which are illustrated with applications, and briefly describes the challenges, drawbacks, also pros and cons case by case. In the end, each case leads to conclusions on the criteria of application of methods as they related to SPE, SEC and PRMS. In this study, "reserve and resource estimation" of unconventional reservoirs is investigated. The techniques are described by giving their methodology, as well as identifying the crucial parameters and the key factors of the applying procedure for the estimations. For instance, the main key factor of reserves evaluation is consistency and abiding by the rules outlined by SEC, PRMS and other bodies in terms of technical and economic aspects. Currently, some studies includes the certain examples of reserve evaluation methods in conventional reservoirs, and limited number of works in unconventionals. However, there is no study which is not only outlines the key elements in one study, but also deducts lessons from the real field applications that will shed light on the utilization of the methods in the future applications. This study will close the gap and become a reference study in unconventional oil industry.

Machine learning models have worked as a robust tool in forecasting and optimization processes for wells in conventional, data-rich reservoirs. In unconventional reservoirs however, given the large ranges of uncertainty, purely data-driven, machine learning models have not yet proven to be repeatable and scalable. In such cases, integrating physics-based reservoir simulation methods along with machine learning techniques can be used as a solution to alleviate these limitations. The objective of this study is to provide an overview along with examples of implementing this integrated approach for the purpose of forecasting Estimated Ultimate Recovery (EUR) in shale reservoirs. This study is solely based on synthetic data. To generate data for one section of a reservoir, a full-physics reservoir simulator has been used. Simulated data from this section is used to train a machine learning model, which provides EUR as the output. Production from another section of the field with a different range of reservoir properties is then forecasted using a physics-based model. Using the earlier trained model, production forecasting for this section of the reservoir is then carried out to illustrate the integrated approach to EUR forecasting for a section of the reservoir that is not data rich. The integrated approach, or hybrid modeling, production forecasting for different sections of the reservoir that were data-starved, are illustrated. Using the physics-based model, the uncertainty in EUR predictions made by the machine learning model has been reduced and a more accurate forecasting has been attained. This method is primarily applicable in reservoirs, such as unconventionals, where one section of the field that has been developed has a substantial amount of data, whereas, the other section of the field will be data starved. The hybrid model was consistently able to forecast EUR at an acceptable level of accuracy, thereby, highlighting the benefits of this type of an integrated approach. This study advances the application of repeatable and scalable hybrid models in unconventional reservoirs and highlights its benefits as compared to using either physics-based or machine-learning based models separately.