This study offered a detailed review of data sciences and machine learning (ML) roles in different petroleum engineering and geosciences segments such as petroleum exploration, reservoir characterization, oil well drilling, production, and well stimulation, emphasizing the newly emerging field of unconventional reservoirs. The future of data science and ML in the oil and gas industry, highlighting what is required from ML for better prediction, is also discussed. This study also provides a comprehensive comparison of different ML techniques used in the oil and gas industry. With the arrival of powerful computers, advanced ML algorithms, and extensive data generation from different industry tools, we see a bright future in developing solutions to the complex problems in the oil and gas industry that were previously beyond the grip of analytical solutions or numerical simulation. ML tools can incorporate every detail in the log data and every information connected to the target data. Despite their limitations, they are not constrained by limiting assumptions of analytical solutions or by particular data and/or power processing requirements of numerical simulators. This detailed and comprehensive study can serve as an exclusive reference for ML applications in the industry. Based on the review conducted, it was found that ML techniques offer a great potential in solving problems in almost all areas of the oil and gas industry involving prediction, classification, and clustering. With the generation of huge data in everyday oil and gas industry activates, machine learning and big data handling techniques are becoming a necessity toward a more efficient industry.

Drilling issues such as shale hydration, high-temperature tolerance, torque and drag are often resolved by applying an appropriate drilling fluid formulation. Oil-based drilling fluid (OBDF) formulations are usually composed of emulsifiers, lime, brine, viscosifier, fluid loss controller and weighting agent. These additives sometimes outperform in extended exposure to high pressure high temperature (HPHT) conditions encountered in deep wells, resulting in weighting material segregation, high fluid loss, poor rheology and poor emulsion stability. In this study, two additives, oil wetter and rheology modifier were incorporated into the OBDF and their performance was investigated by conducting rheology, fluid loss, zeta potential and emulsion stability tests before and after hot rolling at 16 h and 32 h. Extending the hot rolling period beyond what is commonly used in this type of experiment is necessary to ensure the fluid’s stability. It was found that HPHT hot rolling affected the properties of drilling fluids by decreasing the rheology parameters and emulsion stability with the increase in the hot rolling time to 32 h. Also, the fluid loss additive’s performance degraded as rolling temperature and time increased. Adding oil wetter and rheology modifier additives resulted in a slight loss of rheological profile after 32 h and maintained flat rheology profile. The emulsion stability was slightly decreased and stayed close to the recommended value (400 V). The fluid loss was controlled by optimizing the concentration of fluid loss additive and oil wetter. The presence of oil wetter improved the carrying capacity of drilling fluids and prevented the barite sag problem. The zeta potential test confirmed that the oil wetter converted the surface of barite from water to oil and improved its dispersion in the oil.

A continuous growth in the global economy and population requires a sustainable energy supply. Maximizing recovery factor out of the naturally occurring hydrocarbons resources has been an active area of continuous development to meet the globally increasing demand for energy. Coalbed methane (CBM), which is one of the primary resources of natural gas, associates complex storage mechanisms and requires some advanced recovery techniques, rendering conventional reserve assessment methods insufficient. This work presents a literature review on CBM in different aspects. This includes rock characteristics such as porosity, permeability, adsorption capacity, adsorption isotherm, and coal classification. In addition, CBM reservoirs are compared to conventional reservoirs in terms of reservoir quality, reservoir properties, accumulation, and water/gas saturation and production. Different topics that contribute to the production of CBM reservoirs are also discussed. This includes production mechanisms, well spacing, well completion, and petrophysical interpretations. The main part of this work sheds a light on the available techniques to determine initial-gas-in-place in CBM reservoirs such as volumetric, decline curve, and material balance. It also presents the pros and cons of each technique. Lastly, common development and economic challenges in CBM fields are listed in addition to environmental concerns.

Converting data to actionable information through continuous oil production monitoring is a fundamental part of any production optimization strategy. The development of Intelligent Field technology has remarkably contributed to the upgrading of production surveillance framework and provided an extended access to real-time data. This same technology is still in its infancy when it comes to multiphase mass metering and field practicality issues. As for conventional fields where the unavailability of continuous data flow is not considered out of norm, the high uncertainty in oil production rate estimation and allocation is very well expected. The main source of this uncertainty is the reliance on sporadic welltest data and empirical multiphase flow correlations to allocate liquid production rate. Critical and subcritical multiphase flow choke performance is predicted using well-known correlations that are based on specific datasets characterized by a specific field or hydrocarbon type. Case studies where those correlations are matched with different production data and used later to predict the choke performance are present in the literature. Yet, the oil industry is faced with many challenges because of the limited accuracy of those predictions. The complexity of multiphase flow behavior and the irregularities in operational conditions can explain such low capability of those correlations particularly on field data. Artificial intelligence (AI) tools and techniques for so-called artificial neural networks, fuzzy logic and functional networks were employed to develop data-driven oil flow rate computational models for both critical and subcritical flow conditions. These AI models were trained and tested exploiting 595 production rate tests from 31 different wells. The prediction results showed a strong correlation with actual field data and promised a reliable tool/methodology to estimate oil flow rate as a function of operational conditions and choke size. This paper presents an engineering look at the inclusion of AI data-driven models in the production surveillance system to enhance welltest data validation and reduce the uncertainties in production allocation.

Enzyme-induced calcium carbonate precipitation (EICP) techniques are used in several disciplines and for a wide range of applications. In the oil and gas industry, EICP is a relatively new technique and is actively used for enhanced oil recovery applications, removal of undesired chemicals and generating desired chemicals in situ, and plugging of fractures, lost circulation, and sand consolidation. Many oil- and gas-bearing formations encounter the problem of the flow of sand grains into the wellbore along with the reservoir fluids. This study offers a detailed review of sand consolidation using EICP to solve and prevent sand production issues in oil and gas wells. Interest in bio-cementation techniques has gained a sharp increase recently due to their sustainable and environmentally friendly nature. An overview of the factors affecting the EICP technique is discussed with an emphasis on the in situ reactions, leading to sand consolidation. Furthermore, this study provides a guideline to assess sand consolidation performance and the applicability of EICP to mitigate sand production issues in oil and gas wells.