Search

Over time, oil consumption has increased along with a continuous demand for petroleum products that require finding ways to increase hydrocarbon production more economically and effectively. So, enhanced oil recovery technologies are believed to be very promising and will serve as a key to meeting the future energy demand. This paper aims to introduce an innovative method to boost the EOR by using two novel types of surfactants synthesized from sulfonamide derivatives. Types I and II surfactants were analyzed using Fourier transform infrared spectroscopy, and their characterization was further performed using 1 H NMR and 13 C NMR spectroscopy. Additionally, the evaluation of these surfactants included interfacial tension measurements at concentrations up to 0.9 wt %. The combination of types I and II surfactants with alkaline (NaOH) was also investigated by the measurements of interfacial tension. A series of coreflood and sandpack tests under high-salinity conditions were carried out to assess the effects of a surfactant alone and alkaline-surfactant as a combination on improving oil recovery. The rock wettability was evaluated using relative permeability saturation curves, and the oil displacement efficiency was determined using fractional flow curves. The coreflood results demonstrated that alkaline-surfactant flooding with the chemical formula 0.2 wt % surfactant type II plus 0.5 wt % NaOH achieved a higher oil recovery of 74% OOIP compared to surfactant flooding with the chemical formula 0.5 wt % surfactant type II (64% OOIP) and waterflooding (saline solution with a 35,000 ppm salinity: 48% OOIP). Moreover, the experimental results showed that under both core and sandpack flood conditions, there was a noticeable reduction in oil-water interfacial tension, a change in rock wettability to more water-wet, and higher efficiency of oil displacement when alkaline was added to the surfactant. Based on current research, the alkaline-surfactant formulation is strongly recommended for chemical flooding because of its high efficacy and relatively low cost., Competing Interests: The authors declare no competing financial interest., (© 2023 The Authors. Published by American Chemical Society.)

The world is gradually moving toward a severe energy crisis, with an ever-increasing demand for energy overstepping its supply. Therefore, the energy crisis in the world has shed important light on the need for enhanced oil recovery to provide an affordable energy supply. Inaccurate reservoir characterization may lead to the failure of enhanced oil recovery projects. Thus, the accurate establishment of reservoir characterization techniques is required to successfully plan and execute the enhanced oil recovery projects. The main objective of this research is to obtain an accurate approach that can be used to estimate rock types, flow zone indicators, permeability, tortuosity, and irreducible water saturation for uncored wells based on electrical rock properties that were obtained from only logging tools. The new technique is obtained by modifying the Resistivity Zone Index (RZI) equation that was presented by Shahat et al. by taking the tortuosity factor into consideration. When true formation resistivity ( R t ) and inverse porosity (1/Φ) are correlated on a log-log scale, unit slope parallel straight lines are produced, where each line represents a distinct electrical flow unit (EFU). Each line's intercept with the y -axis at 1/Φ = 1 yields a unique parameter specified as the Electrical Tortuosity Index (ETI). The proposed approach was validated successfully by testing it on log data from 21 logged wells and comparing it to the Amaefule technique, which was applied to 1135 core samples taken from the same reservoir. Electrical Tortuosity Index (ETI) values show marked accuracy for representing reservoir compared with Flow Zone Indicator (FZI) values obtained by the Amaefule technique and Resistivity Zone Index (RZI) values obtained by the Shahat et al. technique, with correlation coefficients of determination ( R 2 ) values equal to 0.98 and 0.99, respectively. Hence, by using the new technique, the Flow Zone Indicator, permeability, tortuosity, and irreducible water saturation were estimated and then compared with the obtained results from the core analysis, which showed a great match with the R 2 -values of 0.98, 0.96, 0.98, and 0.99, respectively., Competing Interests: The authors declare no competing financial interest., (© 2023 The Authors. Published by American Chemical Society.)

Chemical flooding using a polymer and/or surfactant has been widely applied in oilfields worldwide for enhanced oil recovery. Chemical adsorption in reservoirs has a significant effect on the rock permeability and wettability and hence can affect the overall oil production. In this work, two chemicals, namely, the xanthan gum (XG) biopolymer and sodium dodecylbenzenesulfonate (SDBS) anionic surfactant, were used individually as displacement fluids. The amount of chemical adsorption on the rock surface and the residual resistance factor (permeability reduction) were calculated throughout the flooding experiments using an unconsolidated sandstone (SS) pack model. The effects of the injected chemicals' concentration and reservoir salinity on adsorption capacity have been examined. Additionally, the effect of the addition of nanosilica particles (NSPs) to the injected fluid on the rock adsorption was also investigated. The results showed that the amount of XG and SDBS adsorption on the rock surface increased, albeit to a different extent, by increasing the chemical concentration at the applied salinities (0, 3.5, 5, and 10%) of the displacement fluids. Also, the permeability reduction increased with the increase in XG and SDBS concentrations; however, permeability reduction due to SDBS flooding was lower than that of XG in SS. The use of NSPs as a coinjectant to the XG and SDBS displacement fluids increased the adsorption on the SS rock. A plausible mechanism for the adsorption of the XG/NSP and SDBS/NSP blends on the SS surface was proposed. A density function theory calculation was employed to establish a relation between the adsorptivity of NSPs on SDBS and XG and the total energy and dipole moment of the molecules., Competing Interests: The authors declare no competing financial interest., (© 2022 The Authors. Published by American Chemical Society.)

Precise prediction of pore pressure and fracture pressure is a crucial aspect of petroleum engineering. The awareness of both fracture pressure and pore pressure is essential to control the well. It helps in the elimination of the problems related to drilling, waterflooding project, and hydraulic fracturing job such as fluid loss, kick, differential sticking, and blowout. Avoiding these problems enhances the performance and reduces the cost of operation. Several researchers proposed many models for predicting pore and fracture pressures using well log information, rock strength properties, or drilling data. However, some of these models are limited to one type of lithology such as clean and compacted shale formation, applicable only for the pressure generated by under compaction, and some of them cannot be used in unloading formations. Recently, artificial intelligence techniques showed a great performance in petroleum engineering applications. Hence, in this paper, two artificial neural network models are developed to estimate both pore pressure and fracture pressure through the use of 2820 data sets obtained from drilling data in mixed lithologies of sandstone, carbonate, and shale. The proposed artificial neural network (ANN) models achieved accurate estimation of pore and fracture pressures, where the coefficients of determination ( R 2 ) for pore and fracture pressures are 0.974 and 0.998, respectively. Another data set from the Middle East was used to validate the developed models. The models estimated the pore and fracture pressures with high R 2 values of 0.90 and 0.99, respectively. This work demonstrates the validity and reliability of the developed models to calculate pore and fracture pressures from real-time surface drilling parameters by considering the formation type to overcome the limitation of previous models., Competing Interests: The authors declare no competing financial interest., (© 2022 The Authors. Published by American Chemical Society.)

Water saturation assessment is recognized as one of the most critical aspects of formation evaluation, reserve estimation, and prediction of the production performance of any hydrocarbon reservoir. Water saturation measurement in a core laboratory is a time-consuming and expensive task. Many scientists have attempted to estimate water saturation accurately using well-logging data, which provides a continuous record without information loss. As a result, numerous models have been developed to relate reservoir characteristics with water saturation. By expanding the use and advancement of soft computing approaches in engineering challenges, petroleum engineers applied them to estimate the petrophysical parameters of the reservoir. In this paper, two techniques are developed to estimate the water saturation in terms of porosity, permeability, and formation resistivity index through the use of 383 data sets obtained from carbonate core samples. These techniques are the nonlinear multiple regression (NLMR) technique and the artificial neural network (ANN) technique. The proposed ANN model achieved outstanding performance and better accuracy for calculating the water saturation than the empirical correlation using NLMR and Archie equation with a high coefficient of determination ( R 2 ) of 0.99, a low average relative error of 1.92, a low average absolute relative error of 13.62, and a low root mean square error of 0.066. To the best of our knowledge, the current research establishes a novel foundation using the ANN model in the estimation of water saturation., Competing Interests: The authors declare no competing financial interest., (© 2022 The Authors. Published by American Chemical Society.)

Successful drilling operations require optimum well planning to overcome the challenges associated with geological and environmental constraints. One of the main well design programs is the mud program, which plays a crucial role in each drilling operation. Researchers focus on modeling the rheological properties of the drilling fluid seeking for accurate and real-time predictions that confirm its crucial potential as a research point. However, only substantial studies have real impact on the literature. Several AI-based models have been proposed for estimating mud rheological properties. However, most of them suffer from non-being field applicable attractive due to using non-readily field parameters as input variables. Some other studies have not provided a comprehensive description of the model to replicate or reproduce results using other datasets. In this study, two novel robust artificial neural network (ANN) models for estimating invert emulsion mud plastic viscosity and yield point have been developed using actual field data based on 407 datasets. These datasets include mud plastic viscosity (PV), yield point (YP), mud temperature ( T ), marsh funnel viscosity (MF), and solid content. The mathematical base of each model has been provided to provide a clear means for models' replicability. Results of the evaluation criteria depicted the outstanding performance and consistency of the proposed models over extant ANN models and empirical correlations. Statistical evaluation revealed that the plastic viscosity ANN model has a coefficient of determination ( R 2 ) of 98.82%, a root-mean-square error (RMSE) of 1.37, an average relative error (ARE) of 0.12, and an absolute average relative error of 2.69, while for yield point, this model has a coefficient of determination ( R 2 ) of 94%, a root-mean-square error (RMSE) of 0.76, an average relative error (ARE) of -0.67, and an absolute average relative error of 3.18., Competing Interests: The authors declare no competing financial interest., (© 2021 The Authors. Published by American Chemical Society.)

Improving oil production for high-salinity reservoirs using polymer flooding is challenging due to chemical and mechanical degradations. This study developed two biodegradable biopolymers based on graft copolymerization of guar gum (GG) with two different co-monomers, which are acrylamide (Am) and 2-acrylamido-2-methylpropane sulfonic acid (AMPS), and cross-linked by N,N '-methylene bisacrylamide (MBA) to face these challenges. The newly synthesized guar gum-based hydrogels, GG- g -poly(Am-AMPS) (GH) and GG- g -poly(Am-AMPS)/Biochar (GBH composite), were evaluated as potential candidates for enhanced oil recovery (EOR) under high-salinity conditions. Fourier transform infrared (FTIR) spectroscopy and thermogravimetric analysis (TGA) of the synthesized hydrogels were investigated, and their rheological properties were measured at room temperature. Both GH and GHB display a shear-thinning performance. In polymer flooding experiments, guar gum hydrogel (GH) and guar gum/biochar composite hydrogel (GHB) showed a remarkable influence on delaying the water breakthrough and proved to be effective biopolymers for enhanced oil recovery in high-salinity reservoirs. At the optimum concentration of 5 g/L, GH flooding achieved maximum oil recoveries of 70.53 and 72.11% in secondary and tertiary recovery processes, respectively. Meanwhile, the waterflooding process achieved an ultimate oil recovery of 58.42%. GHB flooding at optimum concentration, 2 g/L, increased the amount of oil recovery by 8.95% in tertiary recovery compared to waterflooding. Furthermore, GH (5 g/L) and GHB (2 g/L) slightly enhanced the rock water wettability as confirmed by contact angle measurements for GH and the relative permeability saturation curves for GH and GHB., Competing Interests: The authors declare no competing financial interest., (© 2021 The Authors. Published by American Chemical Society.)