Currently, engineers are using numerical correlations to describe the flow of oil and gas through chokes. These numerical correlations are not 100% accurate, as indicated by other studies, so there is a need to find a better approach to describe and calculate the choke size. Artificial intelligence (AI) can be used for better results. In this study, AI was used to estimate the optimum choke size that is required to meet the desired flow rate. Four techniques are used in this study: artificial neural networks, fuzzy logic (FL), support vector machines, and functional networks. Results obtained using these techniques were compared. After researching each technique, FL was found to give the best predictions.

The process of “hysteresis” has widely attracted the attention of researchers and investigators due to its usage in many disciplines of science and engineering. Economics, physics, chemistry, electrical, mechanical, and petroleum engineering are some examples of disciplines that encounter hysteresis. However, the meaning of hysteresis varies from one field to another, and therefore, many definitions occur for this phenomenon depending on the area of interest. The “hysteresis” phenomenon in petroleum engineering has gained the attention of researchers and investigators lately, because of the role that plays in reservoir engineering and reservoir simulation. Hysteretic effects influence reservoir performance. Therefore, an accurate estimation of rock and fluid property curves has an essential role in evaluating hydrocarbon recovery processes. In this paper, a comprehensive review of research and growth on the hysteresis of wettability for its applications in petroleum engineering is reported. Also, theoretical and experimental investigations of hysteresis of wettability are compared and discussed in detail. The review highlights a range of concepts in existing models and experimental processes for wettability hysteresis. Furthermore, this paper tracks the current development of hysteresis and provides insight for future trends in the research. Finally, it reveals an outlook on the research challenges and weaknesses of hysteresis of wettability.

The efficient use of available energy in hydrocarbon extraction processes is essential to reducing overall emissions in the petroleum industry. The inefficient design of an extraction process leads to higher emissions per unit mass of hydrocarbon recovery. Fluid friction and heat transfer are irreversible processes that are vital in decreasing the overall system’s operational efficiency. To reduce these irreversible energy losses in the petroleum reservoir production’s life, contributing factors such as the characteristic features of a reservoir formation, reservoir fluids, and production rate are investigated in this paper. This study examines irreversible energy loss in porous media and wellbore formations using entropy generation minimization at various stages of production and thermodynamic conditions, eventually achieving higher hydrocarbon recovery factors. Entropy production is used to develop predictive models that calculate reservoir and wellbore energy losses for multiphase flow. The proposed models consider oil and water as the working fluids in a porous medium and a wellbore. This paper also investigates the thermophysical effects around the wellbore by incorporating Hawkin’s model. A sensitivity analysis assessed the impact of rock and fluid properties and thermodynamic conditions such as temperature, wettability, and capillary pressure on the total entropy generation. The findings reveal that the capillary pressure significantly impacts the oil and water recovery factor and total entropy production. Additionally, the capillary pressure strongly influences the reservoir production life. The two-phase models show that as the recovery factor increases, the total entropy production decreases at lower production rates. This article helps to address the impact of irreversible processes on multiphase hydrocarbon reservoir operational efficiency. Furthermore, the results obtained from the numerical-simulation model open up a new research area for scholars to maximize the recovery factor using entropy generation minimization in heterogeneous reservoirs.

To maximize production efficiency, natural gas flow via surface well chokes must be optimized. The nonlinear character of this flow frequently causes problems for conventional empirical correlations and mechanistic models. To accurately forecast gas flow rates using field data, this study develops an Artificial Neural Network (ANN) model that considers temperature, gas gravity, choke size, and pressures. The main innovation is that the optimized network is used to derive an exact, closed-form empirical equation, going beyond the typical "black-box" use of ANN. This equation enables the estimation of flow rate in real-time without requiring the execution of the ANN model, providing engineers with a valuable tool at present. The 5-neuron optimized ANN demonstrated remarkable accuracy, with training and testing average absolute percentage errors (AAPE) below 2% and a correlation coefficient (R) over 0.99. When tested on unknown data, the resultant equation performed well (R=0.999, AAPE=2.78%), outperforming conventional techniques in terms of generalization and predictive power. By connecting data-driven analytics with field operational realities for well management, this research represents a significant leap forward.

Hysteresis is proven to have a significant role in petroleum fluids and other disciplines for better understanding and evaluation. This shows a need to be explicit about precisely what is meant by the word "hysteresis." For a long time, the term hysteresis has been used and has attracted the attention of most researchers and investigators. Despite its common usage, hysteresis is used in different disciplines to mean different things. Thus, hysteresis has many definitions depending on the book or paper's area of interest. While various definitions of the term 'hysteresis' have been suggested, this paper will focus on the definition in the oil and gas industry. Hysteretic impacts petroleum fluids either positively or negatively. Therefore, accurately estimating fluid properties curves is vital in evaluating hydrocarbon recovery processes. This paper addresses and discusses a comprehensive review of the hysteresis of different petroleum fluid properties and their applications. This paper reviews many fluid properties of hysteresis and investigates them experimentally. Numerous laboratory studies in hysteresis are present in the literature and critically reviewed and highlighted in this research. This paper aims to review the experimental processes of fluid hysteresis extensively. To satisfy this aim, this paper offers insights into and explanations for experiments that have been used in fluid hysteresis. The outcomes highlight some missing concepts of the existing models and experimental processes for fluid hysteresis. Furthermore, this paper tracks the current development of hysteresis and gives insight into the future trends in the application of hysteresis.