This study conducted a radial flow experiment to investigate the existence of non-Darcy flow and calculate the non-Darcy “inertia” coefficient; the experiment was performed on seven cylindrical perforated artificial porous media samples. Two hundred thirty-one runs were performed, and the pressure drop was reported. The non-Darcy coefficient $\beta$ was calculated and compared with available in the literature. The results showed that the non-Darcy coefficient decreased nonlinearly and converged on a value within a specific range as the permeability increased. Nonetheless, it was found that the non-Darcy flow exists even in the very low flow rate deployed in this study. In addition, it has been found that the non-Darcy effect is not due to turbulence but also the inertial effect. The existence of a non-Darcy flow was confirmed for all the investigated samples. The Forchheimer numbers for airflow at varied flow rates are determined using experimentally measured superficial velocity, permeability, and non-Darcy coefficient.

This study presents a comprehensive investigation into the application of reservoir simulation and machine learning techniques to improve the understanding and prediction of reservoir behavior, focusing on the Sarir C-Main field. The research uses various data sources to develop robust reservoir static and dynamic models, including seismic cubes, well logs, base maps, check shot data, and production history. The methodology involves data quality control, log interpretation, seismic interpretation, horizon and surface interpretation, fault interpretation, gridding, domain conversion, property and petrophysical modeling, well completion, fluid model definition, and rock physics functions. History matching and prediction are performed using simulation cases, and machine learning techniques such as data gathering, cleaning, dynamic time warping (DTW), long short-term memory (LSTM), and transfer learning are applied. The results obtained through the Petrel simulation demonstrate the effectiveness of depletion strategy, history matching, and completion in capturing reservoir behavior. Furthermore, the machine learning techniques, specifically DTW and LSTM, exhibit promising results in predicting oil production. The study concludes that machine learning approaches, such as the LSTM model, offer distinct advantages. They require significantly less time and can yield reliable predictions. By leveraging the power of transfer learning, accurate predictions can be achieved efficiently when limited data are available, offering a more streamlined and practical alternative to traditional reservoir simulation methods.

Darcy’s law has long been used to describe the flow in porous media. Despite the progress that took place in oil production industry research, it became clear that there is a loss of pressure, especially in the area near the wellbore region, where Darcy’s law is not applicable. For this reason, Forchheimer presented his equation in 1910, where he added a new term to Darcy’s law dealing with pressure loss due to inertial forces by introducing a new term, the $\beta$ coefficient, into the equation. This paper presents a study of fluid flow through porous media, where water was used as a working fluid. Furthermore, the characteristics of the non-Darcy flow were analyzed by presenting the corresponding pressure and velocity gradient curves for each pressure. Extensive analysis indicates that many of the correlations available in the literature either have defective units or are the product of a small number of experiments. In this study, we benefit from relatively large samples, the radial flow, and the perforation in the middle of the samples. The properties of the samples were measured using mercury intrusion porosimetry. It was found that there is a direct relationship between the porosity and the grain’s size; the greater the size of the grains, the greater the porosity, and vice versa. The non-Darcy coefficient term, β, is found to be inversely proportional to the porosity and permeability. In a previous study, the β was investigated for compressible flow scenarios; however, this study calculated it for an incompressible flow. Finally, by analyzing the β values of both studies, we could deduce new novelty correlations for the β coefficient term, where the permeability, porosity, and tortuosity are included.

Oil and gas well productivity can be affected by a number of different skin factors, the combined influences of which contribute to a well’s total skin factor. The skin caused by deviated wells is one such well-known factor. The present study aimed to investigate skin effects caused by deviated well slants when considering vertical-to-horizontal permeability anisotropy. The research employed computational fluid dynamics (CFD) software to simulate fluid flows in inclined wells through the injection of water with Darcy flow using 3D geometric formations. The present work investigates the effects of four main characteristics—namely, the permeability anisotropy, wellbore radius, reservoir thickness, and deviation angle—of open-hole inclined wells. Additional investigations sought to verify the effect of the direction of perforations on the skin factor or pressure drop in perforated inclined wells. In the case of an inclined open hole well, the novel correlation produced in the current study simplifies the estimation of the skin factor of inclined wells at different inclination angles. Our comparison indicates good agreement between the proposed correlation and available models. Furthermore, the results demonstrated a deviation in the skin factor estimation results for perforated inclined wells in different perforation orientation scenarios; therefore, existing models must be improved in light of this variance. This work contributes to the understanding and simulation of the effects of well inclination on skin factor in the near-wellbore region.

The study presented in this paper investigates issues around four-phase (oil/CO2/water/sand) flows occurring within subsea pipelines. Multi-phase flows are generally the norm, as production fluid from reservoirs typically include sand with the water. However, these multi-phase flow mixtures, whether three- or four-phase, are at risk of forming slug flows. The inclusion of sand in

Division / Sponsors: Ocean, Offshore, and Arctic Engineering (OOAE)