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In recent years, the oil and gas sector has been moving towards green production methods to achieve net-zero emission goals. Governments and corporations have started large-scale initiatives to deploy advanced technologies to reduce carbon footprints and prevent global warming. Herein, we have explored the emerging techniques and methods used in reducing the effects of gas emissions in the oil and gas industry. The transition process from hydrocarbons to renewable energy resources, including solar thermal applications for EOR, thermal energy extraction from hydrocarbon reservoirs, hydrogen generation strategies, and CO2 EOR and storage applications, has also been discussed. Literature information and publicly available data have paved the way to provide the theoretical background, the rationale of use, screening and selection criteria, challenges, and workarounds for these novel energy sources. Systems to integrate green methods into oil and gas processes appear in detail, from screening to implementation. Then, the technical information for integrating these resources under multiple conditions that affect the system's efficiency, such as weather, seasonal temperature changes, wind, and solar exposure, have been investigated. Moreover, added benefits of such incorporation strategies, such as improved economics with minimal effects on capital intensiveness or other burdens on the overall economy, have also been addressed. The transition from fossil fuels to renewable and greener energy resources provided the underlying motivation for this study.

Fluid transport can be improved by nanoparticles when they help stimulate a reservoir's rheological properties, which involve flow, viscosity, and permeability, among other parameters. First, this work reviews the literature regarding nanotechnology in the oil and gas sector. Then, it examines a few potential nanoparticle applications that have shown varying degrees of potential to improve colloid transport mechanisms in porous media. This list includes, but is not limited to, magnesium oxide, zinc oxide, silver, silicon dioxide, pyroelectric nanoparticles, and carbon nanotubes, all of which help stimulate a reservoir, which in turn leads to better fluid transport and an enhanced rate of recovery. The authors find that, compared to a baseline scenario that applies no nanotechnology, silicon dioxide, also known as silica, offers interesting advantages when used in laboratory settings. For example, in the case of low permeability limestones, silica helped transport fluids through the fractured rock at a better rate than without nanoparticles. Similarly, aluminum oxide shows the potential to improve rheological and filtration features inside a reservoir, stabilizing the flow of material from a well. Despite the high promise, however, it is still an early stage for field applications, where only a few trials for the use of nanoparticles have been experimented with, especially in porous media. Nanotechnology has become a favorite topic of research across many disciplines. This work is one of the first to offer a comprehensive look at the literature on nanoparticles in the oil and gas industry while also reviewing the applications of different ultrafine elements and their potential for future research endeavors in reservoir optimization and fluid transport in porous media.

Geothermal sites have unique features that require tailored approaches when it comes to production and recovery forecasts. This article explores the latest technologies, and the current challenges that simulation methods face for these types of systems Objectives are to establish this work as a robust literature reference for researchers searching for a guide to assist their future investigations. At the end of their cycles, low permeability hydrothermal sites, low fracture reservoirs, or over-exploited wells are ideal candidates to become enhanced geothermal systems, but to get to this point; optimization processes need to be performed on these sites. Building from earlier models that measure both wellhead temperature and pressure and considering the important challenges to current geothermal systems modeling, we advance a framework that embraces more novel computational techniques that strive towards capturing 3D fluid flow dynamics, as well as potential interactions between aqueous fluids, gases, and porosity and permeability changes brought by the dissolution and transformation of minerals inside the well. The development of more novel models has improved the capabilities for working with increasingly larger quantities of data while also delivering accurate estimations when some data is missing or incomplete. Additionally, the advent of artificial intelligence techniques has aided engineers in modeling quasi-three-dimensional mass transport and fluid flow dynamics, as well as chemical and physical interactions within low-porosity reservoirs. Our review highlights the appearance of two important mathematical models that rely on nonlinear partial differential equations that cover fluid pressure, enthalpy, and boundary conditions. With that said, capturing those interactions in 3D models that are robust and efficient remains a steep challenge for researchers. Through this work, we ultimately offer a roadmap to developing models to combat these limitations. Geothermal systems have been understudied as some consider these wells afterthoughts within oil and gas operations, but more novel methods can significantly improve reservoir simulation for these sites. This work provides a window into the newest advances and techniques while also providing a framework for their use to engineers looking to optimize them.

The machine learning method, now widely used for predicting well performance from unconventional reservoirs in the industry, generally needs large data sets for model development and training. The large data sets, however, are not always available, especially for newly developed unconventional plays. The objective of this work is to develop an innovative machine learning method for predicting well performance in unconventional reservoirs with a relatively small data set.For a small training data set, the corresponding machine learning model can significantly suffer from so-called overfitting meaning that the model can match the training data but has poor predictivity. To overcome this, our new method averages predictions from multiple models that are developed with the same model input, but different initial guesses of model parameters that are unknowns in a machine learning algorithm and determined in the model training. The averaged results are used for the final model prediction. Unlike traditional ensemble learning methods, each prediction in the new method uses all the input data rather than its subset. We mathematically prove that the averaged prediction provides less model uncertainty and under certain conditions the optimum prediction. It is also demonstrated that the method practically minimizes the overfitting and gives relatively unique prediction. The usefulness of the method is further confirmed by its successful application to the data set collected from less than 100 wells in an unconventional reservoir. Sensitivity results with the trained machine learning model show that the model results are consistent with the domain knowledge regarding the production from the reservoir.

Deep learning approaches can be utilized for production forecasting in cases with complex production profiles. In this work, a deep learning algorithm was developed to use the pertinent production ...

Recently, significant breakthroughs have been made in exploring one specific shale oil field, northeast China. On-site observation reveals that the appearance of oil at the wellhead is only seen a long time after fracturing in many wells. The extended period of purely water production complicates subsurface flow behavior and hinders the increase of medium- and long-term oil production. Strong coupling between phase behavior and relative permeability curves in the reservoir with the near-critical point initial condition restricts the efficient development of this kind of shale oil. The work gives detailed descriptions of the geology and petrophysics background of the target formation. A series of compositional simulation models are constructed to reveal the cause of the late oil breakthrough. The delayed oil breakthrough is highly related to the coexistence of three phases that are not seen in common reservoirs. Such reservoirs’ early-time production behavior is positively associated with the gas-liquid relative permeability curves and initial water saturation. Oil-water relative permeability curves affect the water-cut behavior depending on wetting properties. The potential oil-wet property slows down oil breakthroughs. Conceivably, purely gas and water phases exist due to the nanopore confinement of crude oil phase behavior; thus, the late oil production is barely related to the gas-liquid relative permeability curves. The Q formation is widely developed in Songliao Basin, where the oil and gas generation indexes are promising. Few tectonic movements and large-scale fault zones occurred in this area suitable for oil and gas exploration and development.1,2 The first member of the Q formation contains high-quality source rocks and high hydrocarbon generation potential. The shale formation strata are similar to the Forth Worth basin and Barnett shale stratigraphic assemblage in the United States, containing miscellaneous mineralogies. The shale of the Q formation is felsic; generally, the first member has less clay than the second and third members.3 During the development of G Sag in the first member, deferred oil production appears in many horizontal wells after fracturing. The effluent at the wellhead continues to be pure water phase for a long time. This phenomenon did not occur in China's Longdong shale oil, Mahu tight oil, Jimusar shale oil, and Changqing tight oilfields.4–6 For example, the average oil appearance time in the demonstration area of three-dimensional development of the Mahu tight conglomerate reservoir is just 1.67 days, and the initial production increased rapidly. Similarly, according to our literature survey, such postponed oil production has not been recorded in shale reservoirs in the United States. The late and long-term water production occurrence means that complex phase transition and multiphase flow behavior have occurred in the subsurface, regulating medium- and long-term oil and gas production. The late appearance of oil is closely related to the initial state of the reservoir at the critical point of the phase envelope and the three-phase flow of oil, gas, and water. Generally speaking, the initial pressure of the reservoir is much higher than the saturation pressure, so the oil and water phases coexist, and the oil phase is in a single-phase state.7,8 For many light crude oils in G sag, the initial state of the reservoir is close to the critical temperature and critical pressure point, implying that oil, gas, and water might coexist initially, leading to the unique production flow circumstance after fracturing. The objectives of this study are: 1. provide a relatively comprehensive background petrophysical delineation of the Q formation; 2. establish exploratory reservoir simulation models based on PVT data to interpret the delayed oil production; the inceptive models are compositional with deep insight into the coupled phase behavior and relative permeability curves; 3. investigate possible nanopore confinement on the relative permeability interpretation.

Summary Prediction of well production from unconventional reservoirs is often a complex problem with an incomplete understanding of physics and a considerable amount of data. The most effective way for dealing with it is to use the gray-box approach that combines the strengths of physics-based models and machine learning (ML) used for dealing with certain components of the prediction where physical understanding is poor or difficult. However, the development of methodologies for the incorporation of physics into ML is still in its infancy, not only in the oil and gas industry, but also in other scientific and engineering communities, including the physics community. To set the stage for further advancing the use of combining physics-based models with ML for predicting well production, in this paper we present a brief review of the current developments in this area in the industry, including ML representation of numerical simulation results, determination of parameters for decline curve analysis (DCA) models with ML, physics-informed ML (PIML) that provides an efficient and gridless method for solving differential equations and for discovering governing equations from observations, and physics-constrained ML (PCML) that directly embeds a physics-based model into a neural network. The advantages and potential limitations of the methods are discussed. The future research directions in this area include, but are not limited to, further developing and refining methodologies, including algorithm development, to directly embed physics-based models into ML; exploring the usefulness of PIML for reservoir simulations; and adapting the new developments of how the physics and ML are incorporated in other communities to the well-production prediction. Finally, the methodologies we discuss in the paper can be generally applied to conventional reservoirs as well, although the focus here is on unconventional reservoirs.

Digital transformation brings substantial operational efficiency improvements in asset-heavy and capital-intensive industries like oil and gas. While digital transformation covers a wide array of technologies, this study focuses on the application of unmanned aerial vehicles (UAVs), drones, and robotics in the oil and gas industry. These technologies can unlock tremendous value in oilfield operations. The objective of this study is to examine methods through which this value will be brought to the industry and analyze the impact of these technologies in enabling Industrial Revolution 4.0 in the upstream oil and gas industry. The literature has been thoroughly reviewed to cover the history of UAVs, drones, and robotics in the oil and gas industry along with related technologies transferred from other industries. Focus has been provided on analyzing early-stage and mature technologies and how UAVs, drones, and robotics can be implemented in the oil and gas industry technologies in a repeatable and scalable manner. In addition, real-field applications of these technologies to oil and gas fields in different parts of the world, the incremental benefits each application provides, and the challenges in implementing them are investigated in detail. The innate ability of UAVs, drones, and robotics technologies in interconnecting physical assets with digital solutions is what allows it to unlock huge amounts of value in any given oilfield application. These technologies can be used in areas with a significant risk to personnel, such as deepwater platforms. They can be used in applications that help improve environmental safety, such as methane emissions monitoring, plugging leaks in subsea pipelines, etc. The focus must be given to ensure that customized solutions are developed for each use case so that the true gains these technologies can bring, either in cost reductions, revenue maximization, or enhanced safety, are realized. These technologies can also enable oilfield assets even in remote areas to continuously operate in optimal conditions and ensure positive project economics. While there are many advantages to these technologies, their challenges, such as cybersecurity issues and its impact of physical security of critical assets, are addressed in this study. UAVs, drones and robotics are widely known concepts across the industry, and there are multiple examples of specific use cases of such technologies in the literature. However, there still is a lack of clarity on the specific use-cases, benefits, and potential of such technologies amongst industry professionals. This study bridges the gap by providing a concise overview of these technologies enabling the fourth industrial revolution, including their history, application areas, successful use cases, and future potential.

Hydraulic fracturing is a widely accepted and applied stimulation method in the unconventional oil and gas industry. With the increasing attention to unconventional reservoirs, hydraulic fracturing technologies have developed and improved more in the last few years. This study explores all applications of hydraulic fracturing methods to a great extent. It can be used as a guideline study, covering all the procedures and collected data for conventional reservoirs by considering the limited parameters of unconventional reservoirs.This paper intends to be a reference article containing all the aspects of the hydraulic fracturing method. A comprehensive study has been created by having a wide scope of examinations from the applied mechanisms to the technological materials conveyed from the different industries to utilize this technique efficiently. Furthermore, this study analyses the method, worldwide applications, advantages and disadvantages, and comparisons in different unconventional reservoirs. Various case studies that examine the challenges and pros & cons of hydraulic fracturing are included.Hydraulic fracturing is a promising stimulation technique that has been widely applied worldwide. It is challenging due to the tight and nanoporous nature, low permeability, complex geological structure, and in-situ stress field in unconventional reservoirs. Consequently, economic conditions and various parameters should be analyzed individually in each case for efficient applications. Therefore, this study provides the primary parameters and elaborate analysis of the techniques applied for a successful stimulation under SPECIFIC circumstances and provides a full spectrum of information needed for unconventional field developments. All the results are evaluated and detailed for each field case by providing the principles of applying hydraulic fracturing technologies.Many literature reviews provide different examples of hydraulic fraction methods; however, no study covers and links up both the main parameters and learnings from real cases worldwide. This study will fill this gap and illuminate the application of the hydraulic fracturing method.

Successful heavy oil reservoir management practices, are built on analyzing and accurately predicting the reservoir behavior over time. To enable these practices, the critical component that assures their success is a thorough understanding of reservoir physics. Given the complex nature of heavy oil reservoirs including geomechanical properties, fluid flow behavior, etc., there is a need to develop a repeatable technique that can account for these complexities within an acceptable margin of accuracy. The objective of this study is to conduct a comprehensive review of all the latest technologies and workflows developed for heavy oil reservoir management, so that, it can be used as a single source of reference for the industry. The latest technologies for heavy oil reservoir management, their underlying principles, along with the advantages and limitations for each of the methods in real-world applications, have been reviewed from different parts of the globe. Based on this review, conclusions have been drawn that help select the best criteria for using the latest heavy oil reservoir management techniques. The primary component of successfully applying heavy oil reservoir management methods, lies in accurately representing the reservoir physics. These components include petrophysical properties, fluid flow and geological properties of a given formation. In addition to outlining each of these components, an emphasis has been given to highlight the important criteria that enable the successful application of these methods to a given area. As such, the study will be an information repository catered to assist in developing robust reservoir management workflows for heavy oil reservoirs. While there are other reference, with examples, on heavy oil reservoir management; the uniqueness of this study lies in summarizing key lessons learned from real-field applications of these methods. Within a single source or reference, this study has given the specific focus on summarizing various aspects that are important to successful heavy oil reservoir management processes.

Tracer test is a strong tool that is used to understand the connectivity between injection and production wells in geothermal reservoirs. It is essential to design and implement a tracer test for particular reservoir properties. Inappropriate tracer tests, might cuase wrong reservoir characterization interpretations. This study incorporates in the design, the implementation, and the interpretation of tracer tests in geothermal reservoirs. This study is populated with numerous field applications to ensure better understanding of the subject. The study initially present the types of tracers used in geothermal reservoirs. The appropriate tracer type is selected based on various parameters such as reservoir conditions, economics, type of measurement devices available, minimum detection concentration, environmentally friendly, and stability at reservoir conditions. Once the type of tracer is selected, the amount of tracer to be injected and the tracer sampling frequency are determined based on the distance between wells, mean traveling time, and the desired peak concentration. The tracer is injected as slug/continuous type to the selected injection wells and sampling from production wells. The measured tracer concentrations are then modeled with analytical methods such as the multi-fractures, single fracture, dual-porosity, and homogenous models. Naphthalene sulfonates, is frequently used in high-temperature geothermal reservoirs because of is high resistance and half-life in harsh conditions. Salts such as sodium chloride and potassium chloride are also conservative in harsh conditions; however, a large amount of salt is required to be injected to create an additional concentration in the reservoir brine, which already includes a certain salt concentration. Fluorescein is mostly applicable in low enthalpy reservoirs because of its weakness at high temperatures. Analytical models are matched with field data by using the nonlinear least square method. The most representative reservoir model is determined by evaluating the sum of the squared differences between tracer concentrations of the model and field data. Geothermal reservoirs are generally best matched with multi-fractures and dual-porosity models because of the secondary permeability and porosity of tectonic activities and mineral dissolution mechanisms. This study, provides a detailed information about tracer test design, implementation, and interpretation. It serves as a guidance by including numerous field cases and the latest research about tracers in geothermal.

Historically, renewable energy technologies have been seen as a source of competition by the oil and gas industry. However, the oil and gas industry is now one of the primary sources of investments in renewable energy technologies. The oil and gas industry in California, in particular, has been making significant investments in renewables and leveraging the latest renewable energy technologies to reduce the carbon footprint of oilfield applications. This study analyzes the latest advances in renewable energy solutions, their impact on energy transition efforts in the state, and the potential of further developing conventional and unconventional oil and gas resources in California. This paper has focused on conducting a thorough review of literature related to the latest advances in renewable energy technologies and energy transition efforts in California and the applications of these technologies in reducing the carbon footprint of oilfield activities, along with examples of renewables as a complementary resource to oil and gas. Furthermore, the availability of conventional and unconventional oil and gas resources in the state has been summarized. The potential of developing these resources in the future is analyzed in detail. The literature includes a comprehensive set of references ranging from peer-reviewed journal publications and proceedings to data from market studies and other non-technical publications and market predictions by renowned experts. The position of renewable energy resources either as a competing or complementary source to oil and natural gas in California and the impact of these resources in energy transition efforts in the state are discussed. Examples of the use of renewable energy technologies to improve various processes within oil and gas production operations are provided, and the prospect of further developing conventional and unconventional oil and gas resources in the state has been investigated to conclude the impact of both these energy sources to the regional energy supply and demand dynamics. In addition, the benefits, drawbacks and challenges in scaling up the use of these energy sources are discussed in detail. This study provides a comprehensive overview of the current status of renewable energy resources, energy transition efforts, and the future of conventional and unconventional oil and gas resources in California. It serves as a single source of reference to the oil and gas industry in laying out relevant data and information related to renewable energy resources from the point of view of oil and gas market dynamics and closes the gap in highlighting the latest advances and the future of the energy industry in California.

Carbon capture and underground gas storage technologies have been an area of significant focus in oil and gas industry over the past few decades. They reduce the carbon footprint of oilfield activities and have a critical role in the energy transition efforts by the industry. This study focuses on conducting a thorough review of literature related to latest advances in carbon capture and underground gas storage methods including real-field applications of these technologies. The future potential of these technologies in enabling the oil and gas industry to achieve its ESG goals are also discussed. This chapter examines carbon capture and underground gas storage (CCS) technologies and summarizes various types of CCS techniques such as post-combustion, pre-combustion, and oxyfuel combustion. It describes the important operational principles, efficient methods of technology deployment, and best practices to ensure positive project economics. Additionally, the use of the CCS as a method to enable enhanced oil recovery (EOR) in the oil and gas industry, as well as application areas and challenges faced in the use of CCS in oilfield operations, are discussed. The latest advances in this technology from a global perspective and the future potential of CCS as a critical driver in helping achieve sustainability goals for the oil and gas industry are summarized. Critical factors related to the application of the CCS, including the key decision variables to be considered while implementing projects that uses these technologies are provided to be used as a guideline in decision making. Examples of the use of these technologies to improve the sustainability of oil and gas production operations are provided, as well as the benefits, drawbacks, and challenges in scaling up the use of these technologies in fields are discussed in detail. This study closes the gap in the literature by providing a comprehensive overview of the current status of the CCS technologies and guidelines for their implementation in oil and gas fields. It serves as a single source of information to the oil and gas industry, while laying out relevant data and information related to the CCS technologies, highlighting the latest advances in their application and the future of these technologies from a global perspective.

Accurate estimation of the estimated ultimate recovery (EUR) is critical in decision making processes related to the development of conventional oil reservoirs. Existing methods have limitations when it comes to predicting such long-term production behaviors. This study analyzes the performance of deep learning methods such as long short-term memory (LSTM) neural networks on time-series data, and their effective application to accurately estimate the EUR in conventional reservoirs. Synthetic data that are realistic and representative of many major conventional oil reservoirs were generated for this study. The generated dataset was used by the LSTM model for the purpose of forecasting the EUR. The results of the LSTM model were compared with that of a reservoir simulation model from a full-physics reservoir simulator. EUR forecasts from the physics-based reservoir simulator is used as a benchmark and the LSTM model shows a good predictive accuracy while forecasting the long-term production behavior from a well in a conventional oil reservoir. The LSTM model-based deep learning method can be effectively used with real-field data obtained from wells in conventional reservoirs to accurately predict the EUR, and the study provides a comparative analysis of the results and factors affecting the EUR forecasts from the LSTM model and reservoir simulation model. Deep learning methods such as LSTMs have an inherent advantage in identifying trends in time-series data and making forecasts using the data. The existing literature has a limited number of studies that outline the use of deep learning methods for EUR forecasts and this study covers this gap by providing details analyses, best practices and workflows on the use of such methods for conventional oil reservoirs.

Rate Transient Analysis (RTA) and Decline Curve Analysis (DCA) have been utilized as critical tools in calculation of oil production for unconventional reservoirs. Due to the ultra-low permeability of these wells, time scales of flow regimes are different than those of conventional reservoirs, where transient regimes last longer, and the decline behaviors change, factors that make forecasts more challenging. There are several RTA/DCA methods for originally developed conventional and unconventional reservoirs including recent techniques. However, petroleum engineers require a single comprehensive reference where RTA/DCA methods are covered with detailed explanations, as well as an outline of their assumptions, limitations, strengths, and appropriate applications. This study tackles the lack of such a resource, delivering a comparative work that includes theory, practice, and examples. A comprehensive literature review has been carried out to investigate the RTA/DCA methods for unconventional reservoirs in detail, explore the newest techniques and the different methods repurposed from existing conventional approaches with a longer history of use, robustness, and applicability. We also provide a detailed account of the limitations and the advantages of different methods when applied to different types of fields. We achieve this by developing real field applications in different parts of the world and discussing the challenges and opportunities of each RTA/DCA method for a particular type of well. RTA/DCA methods have shown to be a practical tool for both conventional and unconventional reservoirs, and can be applied across many types of oil and gas wells throughout the world. This work shows the parameters best suited for a successful application of these recovery methods in unconventional sites. Moreover, the evidence collected here will serve as a resource for engineers looking for a summary of the most important criteria to be followed in order to apply oil recovery methods in new wells. We expect that future oil production in unconventional reservoirs can be boosted by the results provided in this work. The novelty of this study centers on the lessons drawn from the real-world applications of RTA/DCA methods like Duong's, or stretched exponential decline, to recover oil from unconventional reservoirs. We expect these lessons to define the proper utilization of distinct methods for different reservoirs in future studies and the field.

With the ongoing paradigm shift in the Oil and Gas industry towards greener alternatives with net-zero objectives, several developing technologies have been recently deployed or proposed as promising solutions. The overall goals are to decrease carbon footprint and improve the projects’ economics and net present value (NPV). This study outlines the latest developments with underlying principles, practices, and economics. This holistic approach encompasses the overall feasibility with the challenges and the benefits.A comprehensive literature survey has been carried out on publicly available data to provide the theoretical background, rationale of use, screening and selection criteria, difficulties, and the workarounds. Systems to integrate the green methods with the respective oil and gas processes appear in detail, from screening to implementation. We outline economics under various scenarios with CAPEX methods and OPEX-intensive approaches to maximize the NPV. The technical details of the integration under multiple conditions that affect the system's efficiency, such as weather, seasonal temperature changes, wind, and solar exposure, have been investigated.Efficient integration of the selected green methods with the associated oil and gas process proves to be a concrete step towards a net-zero objective. Such integration brings additional benefits of improved economics with minimal effects in terms of capital intensiveness or other burdens on the overall economics. These items range from solar thermal applications in heavy oil recovery to heat recovery from the produced fluids, biomass, geothermal, wind, and wave for offshore processes. Cases with multigreen energy methods, such as solar and heat recovery, demonstrate promising outcomes.This article examines some of the latest green methods with various aspects corresponding to the selected oil and gas processes. We specifically focus on energy generation through standalone green methods and extracting energy from oil and gas processes in a greenway. The overall objective is to close the current gap in the literature.

There are documented cases of machine learning being applied to different segments of the oil and gas industry with different levels of success. These successes have not been readily transferred to production forecasting for unconventional oil and gas reservoirs because of sparsity of production data at the early stage of production. Sparsity of unconventional production data is a challenge, but transfer learning can mitigate this challenge. Application of machine learning for production forecasting is challenging in areas with insufficient data. Transfer learning makes it possible to carry over the information gathered from well-established areas with rich data to areas with relatively limited data. This study outlines the background theory along with the application of transfer learning in unconventionals to aid in production forecasting. Similarity metrics are utilized in finding candidates for transfer learning by using key drivers for reservoir performance. Key drivers include similar reservoir mechanisms and subsurface structures. After training the model on a related field with rich data, most of the primary parameters learned and stored in a representative machine or deep learning model can be re-used in a transfer learning manner. By employing the already learned basic features, models with sparse data have been enriched by using transfer learning. The approach has been outlined in a stepwise manner with details. With the help of the insights transferred from related sites with rich data, the uncertainty in production forecasting has decreased, and the accuracy of the predictions increased. As a result, the details of selecting a related site to be used for transfer learning along with the challenges and steps in achieving the forecasts have been outlined in detail. There are limited studies in oil and gas literature on transfer learning for oil and gas reservoirs. If applied with care, it is a powerful method for increasing the success of models with sparse data. This study uses transfer learning to encapsulate the basics of the substructure of a well-known area and uses this information to empower the model. This study investigates the application to unconventional shale reservoirs, which have limited studies on transfer learning.

SPE/IATMI Asia Pacific Oil and Gas Conference and Exhibition 2021, APOG 2021 -- 12 October 2021 through 14 October 2021 -- -- 172791, Reservoir engineering constitutes a major part of the studies regarding oil and gas exploration and production. Reservoir engineering has various duties, including conducting experiments, constructing appropriate models, characterization, and forecasting reservoir dynamics. However, traditional engineering approaches started to face challenges as the number of raw field data increases. It pushed the researchers to use more powerful tools for data classification, cleaning and preparing data to be used in models, which enhances a better data evaluation, thus making proper decisions. In addition, simultaneous simulations are sometimes performed, aiming to have optimization and sensitivity analysis during the history matching process. Multi-functional works are required to meet all these deficiencies. Upgrading conventional reservoir engineering approaches with CPUs, or more powerful computers are insufficient since it increases computational cost and is time-consuming. Machine learning techniques have been proposed as the best solution for strong learning capability and computational efficiency. Recently developed algorithms make it possible to handle a very large number of data with high accuracy. The most widely used machine learning approaches are: Artificial Neural Network (ANN), Support Vector Machines and Adaptive Neuro-Fuzzy Inference Systems. In this study, these approaches are introduced by providing their capability and limitations. After that, the study focuses on using machine learning techniques in unconventional reservoir engineering calculations: Reservoir characterization, PVT calculations and optimization of well completion. These processes are repeated until all the values reach to the output layer. Normally, one hidden layer is good enough for most problems and additional hidden layers usually does not improve the model performance, instead, it may create the risk for converging to a local minimum and make the model more complex. The most typical neural network is the forward feed network, often used for data classification. MLP has a learning function that minimizes a global error function, the least square method. It uses back propagation algorithm to update the weights, searching for local minima by performing a gradient descent (Figure 1). The learning rate is usually selected as less than one. © 2021, Society of Petroleum Engineers.

Unconventional reservoirs, mainly shale oil and natural gas, will continue to significantly help meet the ever-growing energy demands of global markets. Being complex in nature and having ultra-tight producing zones, unconventionals depends on effective well completion and stimulation treatments in order to be successful and economical. Within the last decade, thousands of unconventional wells have been drilled, completed and produced in North America. The scope of this work is exploring the primary impact of completion parameters such as lateral length, frac type, number of stages, proppant and fluid volume effect on the production performance of the wells in unconventional fields. The key attributes in completion, stimulation, and production for the wells were considered in machine learning workflow for building predictive models. Predictive models based on Neural Networks, Support Vector Machines or Decision Tree Based ensemble models, serves as mapping function from completion parameters to production in each well in the field. The completion parameters were analyzed in the workflow with respect to feature engineering and interpretation. This analysis resulted in key performance indicators for the region. Then the optimum values for the best production performing completions were identified for each well. Predictive models in the workflow were analyzed in accuracy and best model is used to understand the impact of completion parameters on the production rates. This study outlines an overall machine learning workflow, from feature engineering to interpretation of the machine learning models to quantify the effects of completion parameters on the production rate of the wells in unconventional fields

With their abundancy and high-quality, it is predicted that fossil fuels will remain as the main resource that will meet the global energy demand in the several upcoming decades. Developments in hydrocarbon recovery technologies, both from conventional and unconventional reservoirs, have substantially contributed to the overall production levels in recent years. However, recovery factors obtained by using the current methods are still considered to be insufficient, and the companies have been looking for new materials and methods to enhance the efficiency and amount of recovery. One of the major issues related to low recovery factors is low permeability of reservoirs. Existence of blockages in pore throats and high level of heterogeneity lowers the mobility of hydrocarbons. In this study, we discuss development of an innovative material to be used as an additive in reservoir injection fluids to remove pore blockages in order to enhance the recovery levels. This additive material is made of pressure-sensitive microspheres loaded with solvents, which can (i) easily disperse in the injection fluid and travel to the low-permeability regions, (ii) break under pressure and confinement to release solvents, and (iii) remove blockages by targeting surroundings, especially asphalt-based particles and grains. This approach relies on the breakage of microcapsules in the confined region and release of the solvents to target blockages in porous media. In other words, the developed microspheres improve permeability of reservoirs as a result of pressure- and confinement-dependent breakage and release of solvents. Preparation of these microspheres was achieved by the encapsulation of solvent (toluene) emulsions in silica-based solid shells. Structure and stability of the solvent-loaded microspheres were examined using a variety of analytical techniques including UV-vis spectroscopy, optical microscopy, scanning electron microscope (SEM) and dynamic light scattering (DLS). It was found that the prepared microspheres possessed smooth surfaces with shell thicknesses in the range of 100-150 nm. Additionally, sand column tests were performed to evaluate the recovery potential of injection fluids in presence of solvent-loaded microspheres. It was shown that the use of solvent encapsulated in microspheres doubled the recovery factor of heavy oil compared to that of free solvent dispersed in the injection fluid. Such enhancement in the recovery factor was related to the release of solvents in localized areas, i.e., confined regions, as a consequence of breakage of microspheres. This novel approach of delivering solvents to low-permeability regions provides a significant driving force to eliminate pore blockages to facilitate mobilization of hydrocarbons trapped in confined spaces.

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.

Summary Capacitance/resistance modeling (CRM) is an empirical waterflood modeling technique based on the signal correlations between injection rates and gross production rates. CRM can satisfactorily estimate the gross (liquid) production rate. The oil-production-rate forecast is based on fitting the empirical oil fractional-flow model, the Leverett (1941) oil fractional-flow model, or the Koval (1963) model to the historical production data. We observed that the oil-production-rate forecast in this approach is less satisfactory. We propose a robust approach that combines CRM gross production prediction with a Buckley-Leverett displacement-theory-based waterflood analytical method—the Y-function method—to calculate the oil fraction flow and to improve the oil prediction capability. The analytical method is based on the results of the historical production performance of either an individual producer or a group of producers in a given area. By using this method, a better understanding can be developed about the production performance, such as the breakthrough time of injected water and possible operational issues, such as water channeling. The analytical model compares oil fractional flow and the cumulative gross production on the producers, yet the value of saturation is not required. As a result, the forecast of the oil-production rate becomes more convenient and straightforward. Sayarpour et al. (2009a) outlined field examples to compare the estimated oil production obtained using the current empirical oil fractional flow-model approach and the analytical Y-function method. The new method provided another effective way to calculate the oil rate in CRM. The results indicated that the new approach improved the accuracy of the oil-rate calculation and proved convenient in field applications. The objective of this study was not to regenerate the gross-rate forecast of CRM, but rather to improve the oil fractional-flow description and oil-production-rate forecast from the gross rate using the Y-function method.

SPE Trinidad and Tobago Section Energy Resources Conference 2021, TTCE 2021 -- 28 June 2021 through 30 June 2021 -- -- 171347, Oil Industry is going green and there is no solid and comprehensive publication that outlines the use of green energies and methods in oil recovery. Thus, this paper is going to close that gap. As there are more environmental restrictions especially in developed countries, inclusion of green energy methods in petroleum recovery processes is very important for the future of these reserves. We will focus on extra/heavy oil as conventional oil is simpler to produce and doesn't need EOR processes that may come with environmental footprints. The objective of this study is to investigate and outline the ‘green’ production and recovery processes of heavy oil recovery in environmentally-sensitive locations where greenhouse gas emissions, type of energy used to extract oil and gas (e.g., generation of steam using natural gas vs solar), environmental impact of surface facilities, transportation of produced oil and gas and other associated materials/chemica ls required for recovery (e.g. solvents for steam injection process) are critical for the operations as well as economics. Copyright 2021, Society of Petroleum Engineers.

Nanotechnologies and particularly state-of-the-art nanomaterials have already gone a long way toward to technological advances in many different industrial and scientific areas such as the materials and manufacturing, pharmaceutical, biomedical, aerospace, electronics, photography, and more recently the energy industries. Likewise, many revolutionary changes have been accomplished by using both nanotechnology and advanced materials in different process areas of the Oil and Gas industry (OG more detailed and accurate information about reservoirs can be reached by using “nanotechnology and advanced materials such as nanosensors”; specifically “fabricated nanoparticles” can be useful for scale inhibition; “structural nanomaterials” can contribute to the development of lighter, more reliable, and long-lasting equipment in the OG and “nanomembranes” can be used for the gas separation and removal of impurities from oil and gas streams. New types of “smart nanofluids” can also be given as an example to the emerging applications of advanced materials and nanotechnology in the petroleum industry for EOR and drilling process. In brief, it can be said that there are many different areas in which nanotechnology can make a contribution to more efficient, much cheaper, and more eco-friendly technologies. In this regard, the importance and urgency of both advanced materials and nanotechnologies with a particular focus on nanotech-based solutions for the O&G industry will be mentioned in this chapter. Moreover, recent developments and advances in both scientific and industrial researches will be briefly reviewed, as well as some case study examples. The present problems/challenges in the O&G industry and their solutions which are obtained by applying nanotechnology and using advanced materials will also be discussed.

21st European Symposium on Improved Oil Recovery, IOR 2021 -- 19 April 2021 through 22 April 2021 -- -- 169492, Production from Unconventional structures is being expected to be the future trend of oil and gas industry. Although the primary recovery values range between 5 to 8%, liquid-rich shale structures has great amount of hydrocarbon in place and it can be produced by EOR applications such as cyclic gas injection that can boost the production up to 1.7 times. This reference study gives a deep review of EOR mechanisms and the stimulation techniques for unconventionals. A comprehensive investigation about the historical development of the concept, the technologies utilized as well as transferred from other industries, adaptness and impetus of the methods in both conventional and unconventional structures is described with the escalating efficacies that these methods ensured for different reservoir structures. The concepts are exemplified with existing worldwide field case studies and applications by implying the advantages, challenges and drawbacks belong to each story. All parameters are discussed and summarized that lead to conclusions on the criteria of application of enhanced technologies. The concept of EOR and Stimulation is a proven area of application which demonstrated itself with its great number of worldwide applications in conventional structures. Key factor of success for an EOR application is to evaluate each case individually to take the suitable actions of technical and economic concerns. This study underlines the theory, key parameters, methodology, challenges and advantages of a successful EOR application for the wells that include intelligent or standard completions in unconventional structures. It aims to serve as a unique reference study which combines of all the aspects of the employed techniques and their usability in specific cases. There are dozens of publications which includes certain examples of Enhanced Oil and Gas technologies, but a detailed study that includes all the aspects of EOR methods suitable to apply in unconventional reservoirs is missing. This study aims to be a reference study by giving all the details from design to result of a successful EOR application in unconventional reservoirs. © IOR 2021 - 21st European Symposium on Improved Oil Recovery.

In the oil and gas industry, there are strict regulations in place with respect to treating produced water from oil and gas facilities prior to disposal or reuse. Currently, technologies in place to meet these regulatory standards are either expensive or not able to filter out smaller-sized contaminants effectively while maintaining an optimal output rate. Nanomembranes and magnetic nanoparticles are excellent environment-friendly candidates that can be used in produced water treatment for filtering contaminants from produced or flowback water in both onshore and offshore operations. Also, in pipelines and oil and gas facilities, efficiently monitoring equipment and components for corrosion prevention and mechanical integrity is a major challenge. Graphene-based sensors have displayed superior performance compared to current technologies for monitoring CO2 vaporization and scale formation in pipelines, especially in terms of cost-effectiveness. Silane-nanoceramic coatings have enabled improved corrosion resistance and thermal insulation in subsea riser systems. In addition, carbon nanotubes have been investigated as a replacement to copper wires in deep-sea power umbilicals. In this chapter, the application of nano-materials and sensors in addressing the aforementioned challenges will be discussed in detail, along with experimental and field studies where they have been successfully deployed.

SPE/IATMI Asia Pacific Oil and Gas Conference and Exhibition 2021, APOG 2021 -- 12 October 2021 through 14 October 2021 -- -- 172791, Digital transformation is one of the most discussed themes across the globe. The disruptive potential arising from the joint deployment of IoT, robotics, AI and other advanced technologies is projected to be over $300 trillion over the next decade. With the advances and implementation of these technologies, they have become more widely-used in all aspects of oil and gas industry in several processes. Yet, as it is a relatively new area in petroleum industry with promising features, the industry overall is still trying to adapt to IR 4.0. This paper examines the value that Industry 4.0 brings to the oil and gas upstream industry. It delineates key Industry 4.0 solutions and analyzes their impact within this segment. A comprehensive literature review has been carried out to investigate the IR 4.0 concept's development from the beginning, the technologies it utilizes, types of technologies transferred from other industries with a longer history of use, robustness and applicability of these methods in oil and gas industry under current conditions and the incremental benefits they provide depending on the type of the field are addressed. Real field applications are illustrated with applications indifferent parts of the world with challenges, advantages and drawbacks discussed and summarized that lead to conclusions on the criteria of application of machine learning technologies. © 2021, Society of Petroleum Engineers.

21st European Symposium on Improved Oil Recovery, IOR 2021 -- 19 April 2021 through 22 April 2021 -- -- 169492, Increasing global trend of unconventional production impelled the oil and gas companies to adapt the EOR mothods to unconventionals to increase the recovery. In this study, a deep review of cyclic gas (Natural Gas, CO2, N2) injection processes and the methodology to apply in unconventional shale reservoirs is described by investigating all the geological, petrophysical, production and reservoir engineering aspects. The significance of each uncertainty and control variable throughout the process is outlined. A full-physics commercial simulator is used to identify the significance of control variables, and also the level of uncertainty which are directly affecting the production and recovery functions. The challenges encountered during implementation of cyclic gas injection processes are outlined in order to provide a comprehensiveand practical implementation perspective rather than only theoretical or a simulation work. Besides, the theory, advantages, drawbacks, benefits are given in details. Results of real cases are being compared and matched with the simulation results. The study indicates the incremental advantages of appliying huff`n puff process in unconventional shale reservoirs by comparing the primary production performance with the performances of cyclic injection cases that uses various gases. Key factors that directly affect the reservoir, completion and operational attitudes are circumstantially given to technically handle a successful project in a feasible way. In the model, the objective function is built by considering the economic parameters to help NPV maximization in a realistic perspective. Scurves and tornado charts is used to visualize the results and illustrate the significance of parameters and ranges of the developed probabilistic economic model. The effect of huff`n puff method and how a successful cyclic injection gives better recovery and feasibility results is clearly shown. Petroleum literature includes several studies related to cyclic gas injection. However, these works are either only focus on the simulation/theoretical parts or only includes a case study that focuses reservoir/production analysis. This comprehensive study closes the gap aims to be a reference by including a deep look into details of the theory and combines it with real field cases and solutions by clearly describing the candidate selection, modeling, physical parameters/key factors, economics, and success stories. © IOR 2021 - 21st European Symposium on Improved Oil Recovery.

The oil and gas well construction is often associated with the unknowns and uncertainties while executing the operations. Because, while drilling, the drilling bit is under the ground where the information is usually gained through interpreting several surface and downhole sensors. Considering the lack of direct access to and visual of the downhole in real-time, the information from these sensors is vital to carry out safe and engineered operations. Typically, these sensors relay basic information such as the pressure in the pipes via a pressure transducer, the weight of the pipes via load transducer, etc. In the well construction process, there are many events where data is not collected or merely the quality is less than desired. Recent want of automation of the well construction process of several oil and gas companies showed the advance and amount of the existing sensors is inadequate. There is a need for more advanced sensors to collect meaningful data and more advanced materials while constructing the well. In the process of well construction, there are many events that the sensors are not used and no data is collected. At the recent years, the oil and gas industry showed a keen interest in automation of the well construction. Researchers started to seek advanced sensors to automate the parts of the process, for example, automation of drilling fluid measurements, automation of pipe handling, etc. An example of drilling fluid measurement automation sensors and materials is presented. The results of this recent successful endeavor motioned the need for more emphasis on the advanced sensors and materials for well construction. In this chapter, the authors are providing a review of select relatively advanced materials and sensors in various disciplines in well construction. The oil and gas industry needs a stronger highlight on the better, advanced sensors and materials to carry out the operations safely and systematically. We hope that this review will increase awareness in the industry to gain more insight into the advanced sensors and materials and use them to accomplish safe and optimized operations.

The enhanced potential properties of nanoparticles as compared to respective parent materials makes them ideal candidates to be used for tackling challenges faced in the oil and gas industry. Nanoparticles display excellent performances over conventional materials in aspects such as: (1) mechanical properties, for instance, the controllable mechanical strength; (2) magnetic properties; (3) chemical properties; and (4) thermal properties. This chapter aims to provide the most up-to-date progress of nanomaterial application in unconventional reservoirs, including the heavy oil reservoirs, shale reservoirs, and gas hydrate formations. The detailed aspects include nanocatalysis and nanofluids for heavy oil recovery and upgrading, nanoparticles’ synthetic function with surfactant in shale oil enhanced oil recovery, nanoparticles for fines migration and formation damage control, wellbore strengthening, and the preliminary progress of nanoparticle application for gas hydrate recovery.

2021 SPE Middle East Oil and Gas Show and Conference, MEOS 2021 -- 28 November 2021 through 1 December 2021 -- -- 177500, EUR (Estimated Ultimate Recovery) forecasting in unconventional fields has been a tough process sourced by its physics involved in the production mechanism of such systems which makes it hard to model or forecast. Machine learning (ML) based EUR prediction becomes very challenging because of the operational issues and the quality of the data in historical production. Geology-driven EUR forecasting, once established, offers EUR forecasting solutions that is not affected by operational issues such as shut-ins. This study illustrates the overall methodology in intelligent fields with real-time data flow and model update that enables optimization of well placement in addition to EUR forecasting for individual wells. A synthetic but realistic model which demonstrates the physics is utilized to generate input data for training the ML model where the spatially-distributed geological parameters including but not limited to porosity, permeability, saturation have been used to describe the production values and ultimately the EUR. The completion is given where the formation characteristics vary in the field that lead to location-dependent production performance leading to well placement optimization based on EUR forecasting from the geological parameters. The algorithm not only predicts the EUR of an individual well and makes decision for the optimum well locations. As the training model includes data of interfering wells, the model is capable of capturing the pattern in the well interference. Even though a synthetic but realistic reservoir model is constructed to generate the data for the aim of assisting the ML model, in practice, it is not an easy task to (1) obtain the input parameters to build a robust reservoir simulation model and (2) understanding and modeling of physics of fluid flow and production in unconventionals is a complex and time-consuming task to build real models. Thus, data-driven approaches like this help to speed up reservoir management and development decisions with reasonable approximations compared to numerical models and solutions. Application of machine learning in intelligent fields is also explained where the models are dynamically-updated and trained with the new data. Geology-driven EUR forecasting has been applied and relatively-new in the industry. In. this study, we are extending it to optimize well placement in intelligent fields in unconventionals beyond other existing studies in the literature. © 2021, Society of Petroleum Engineers

All of the waters of the oil and gas production basins of the world are not saline, but all of them are rich in iodine. Hydrocarbons and iodine-rich waters may be derived from the oil or gas reservoirs in the subsurface to surface by geological events. Surface hydrocarbons seeps, which are direct indicators of a petroleum system in the subsurface may be degraded or volatilizable by atmospheric effects. But iodine is a stable biophilic element and does not volatilize. It is the main reason why iodine is an excellent surface exploration material. Iodine-rich waters provide proving from the surface or near-surface levels of the presence of oil or gas deposits in the subsurface. Iodine-rich waters are rich in mature petroleum hydrocarbons at the same time. Therefore all organic geochemical analyses performed on rock and gas samples can be applied to these hydrocarbons-rich waters. Iodine-rich waters are an especially unique geochemical tool for petroleum exploration in basins/regions, where source rocks are not exposed at surface as outcrops (covered basins) or have been exhausted (depleted). Thus it is a more suitable integrated usage of reservoir-targeted iodine geochemistry (water and soil) and oil in water analysis than the source rock-targeted organic rock and gas geochemistry for oil and gas exploration in active tectonic and geologically complex basins. Also, the integrated method has another significant advantage in the sampling richness for exploration in the basin/regions where a large number of water resources are available. Furthermore, the method identifies the presence of oil and gas deposits from a large number of samples. Through this feature of iodine-rich waters, the chances of discovering commercial hydrocarbon accumulations are increased. By performing iodine analysis on mud samples during drilling, it may be possible to determination whether there is commercial oil or gas in the well. Moreover, the zone(s) perforated in the well and the prediction of the volume of oil produced from it may also be determined. Iodine contents of reservoir waters (petroleum saturation, oil/water ratio) are high in petroliferous basins where source rocks are found containing high organic matter. In this case, the water saturation (water % ratio) of production wells will decrease. Reserves of oilfields increase as the iodine contents of reservoir waters increase. In this chapter, we focused the relationships between iodine and petroleum systems, the usage of iodine hydrogeochemical and pedogeochemical methods in petroleum exploration and production, case studies, and recent advances.

Due to their large surface-area-to-volume ratio and enhanced chemical reactivity, nanoparticles have attracted increasing interest among researchers in the upstream petroleum industry for enhanced oil recovery (EOR) applications which target the recovery of oil left behind by primary and secondary recoveries, by pressure depletion and waterflooding, respectively. In EOR applications, fluids not originally present in the reservoir, such as polymer, surfactant, carbon dioxide or steam, are injected into the reservoir to either increase the sweep efficiency or to mobilize remaining oil so as to reduce the residual oil saturation. In these applications, EOR agents are used to either reduce the mobility of the injected fluid relative to that of oil or to increase the ratio of viscous to interfacial forces, that is, capillary number. In this chapter, we begin with a discussion of the potential use of nanoparticles in the upstream oil industry. This will be followed by a discussion of the unique physics and chemistry of nanoparticles. Then a detailed discussion on how nanoparticles can be used to improve the mobility control and capillary number will be given. It is shown that nanoparticles hold promise in either augmenting existing EOR agents (e.g., surfactant and polymer) or may even replace them to become EOR agents in their own right, although many technical challenges still exist and research is in its early stage. Next the HSE aspects of nanoparticles both in laboratory investigation and field applications will be discussed. We will then conclude with suggestions for future research. With ever increasing advances of nanoparticle technology in many fields of science and engineering, we believe translation of this knowledge to EOR will yield tangible fruits in the not too distant future. However, concerted multidisciplinary efforts will be needed to realize this goal.

SPE Annual Caspian Technical Conference 2020, CTC 2020 -- 21 October 2020 through 22 October 2020 -- -- 164375, 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. Copyright © 2020 Society of Petroleum Engineers.

As major oil and gas companies have been investing in shale oil and gas resources, even though has been part of the oil and gas industry for long time, shale oil and gas has gained its popularity back with increasing oil prices. Oil and gas industry has adapted to the low-cost operations and has started investing in and utilizing the shale oil sources significantly. In this perspective, this study investigates and outlines the latest advances, technologies, potential of shale oil and gas reservoirs as a significant source of energy in the current supply and demand dynamics of oil and gas resources.A comprehensive literature review focusing on the recent developments and findings in the shale oil and gas resources along with the availability and locations are outlined and discussed under the current dynamics of the oil and gas market and resources. Literature review includes a broad spectrum that spans from technical petroleum literature with very comprehensive research using SCOPUS database to other renowned resources including journals and other publications. All gathered information and data are summarized. Not only the facts and information are outlined for the individual type of energy resource but also the relationship between shale oil/gas and other unconventional resources are discussed from a perspective of their roles either as a competing or a complementary source in the industry. In this sense, this study goes beyond only providing raw data or facts about the energy resources but also a thorough publication that provides the oil and gas industry professional with a clear image of the past, present and the expected near future of the shale oil/gas as it stands with respect to other energy resources.Among the few existing studies that shed light on the current status of the oil and gas industry facing the rise of the shale oil are up-to-date and the existing studies within SPE domain focus on facts only lacking the interrelationship between heavy and light oil as a complementary and a competitor but harder-to-recover form of hydrocarbon energy within the era of rise of renewables and other unconventionals. This study closes the gap and serves as an up-to-date reference for industry professionals.

International Petroleum Technology Conference 2020, IPTC 2020 -- 13 January 2020 through 15 January 2020 -- -- 157334, Latest technological developments and applications made optimal control methods usage in optimal well placement in intelligent fields practical and beneficial to increase the production. Effective usage of these methods strongly depends on the detailed evaluation of the economic view and performance in reservoirs that have high uncertainty, particularly. There are several methods of optimization of well placement ranging from classical reservoir engineering to derivative-free and hybrid methods. TNO's Olympus model used globally as a benchmark model in ISAAP-2 Challenge in used. Geological modeling software is coupled with the commercial full-physics reservoir simulator as well as the optimization software in order to produce different geological realizations to represent the geological uncertainty and run the simulation model with differing inputs of optimization and uncertainty in a loop. Results are outlined in detail in a comparative way including comparison to the previous study to illustrate the challenges and benefits of smart wells and optimization of placement of them in intelligent fields. Results indicate that classical reservoir engineering principles still prove useful in the beginning of the optimization process. Then, derivative-free and hybrid methods introduce significant improvement on economics. There are certain challenges in CPU requirements however the state-of-the-art facilities provided significant reduction in runtimes along with the help of the hybrid methods where proxies are built and used for faster runtimes. Despite higher initial capital expenses, smart wells provide significant advantages in recovery and economics compared to that of the conventional wells where these is less control on the production/injection at the layer level. Literature lacks a comprehensive study that takes into account the optimization of well placement in smart fields focusing on smart wells and the all major available methods for optimization. This study closes that gap providing a strong reference building on top of the previous study extending it to intelligent fields which are becoming very common and useful in oil and gas industry in conventional and unconventional applications. Copyright 2020, International Petroleum Technology Conference.