Iranian Journal of Oil and Gas Science and Technology

Abstract In this paper, the potentials of using particles, especially nanoparticles, in enhanced oil recovery is investigated. The effect of different nanoparticles on wettability alteration, which is an important method to increase oil recovery from oil-wet reservoirs, is reviewed. The effect of different kinds of particles, namely solid inorganic particles, hydrophilic or hydrophobic nanoparticles, and amphiphilic nanohybrids on emulsion formation (which is cited as a contributing factor in crude oil recovery) and emulsion stability is described. The potential of nanohybrids for simultaneously acting as emulsion stabilizers and transporters for catalytic species of in situ reactions in reservoirs is also reviewed. Finally, the application of nanoparticles in core flooding experiments is classified based on the dominant mechanism which causes an increase in oil recovery from cores. However, the preparation of homogeneous suspensions of nanoparticles is a technical challenge when using nanoparticles in enhanced oil recovery (EOR). Future researches need to focus on finding out the proper functionalities of nanoparticles to improve their stability under harsh conditions of reservoirs. 

Abstract

Abstract Carbonate reservoirs rock typing plays a pivotal role in the construction of reservoir static models and volumetric calculations. The procedure for rock type determination starts with the determination of depositional and diagenetic rock types through petrographic studies of the thin sections prepared from core plugs and cuttings. In the second step of rock typing study, electrofacies are determined based on the classification of well log responses using an appropriate clustering algorithm. The well logs used for electrofacies determination include porosity logs (NPHI, DT, and RHOB), lithodensity log (PEF), and gamma ray log. The third step deals with flow unit determination and pore size distribution analysis. To this end, flow zone indicator (FZI) is calculated from available core analysis data. Through the application of appropriate cutoffs to FZI values, reservoir rock types are classified for the studying interval. In the last step, representative capillary pressure and relative permeability curves are assigned to the reservoir rock types (RRT) based upon a detailed analysis of available laboratory data. Through the analysis of drill stem test (DST) and GDT (gas down to) and ODT (oil down to) data, necessary adjustments are made on the generated PC curves so that they are representative of reservoir conditions. Via the estimation of permeability by using a suitable method, RRT log is generated throughout the logged interval. Finally, by making a link between RRT’s and an appropriate set of seismic attributes, a cube of reservoir rock types is generated in time or depth domain. The current paper reviews different reservoir rock typing approaches from geology to seismic and dynamic and proposes an integrated rock typing workflow for worldwide carbonate reservoirs.

Abstract The spontaneous imbibition of aquifer/injected brine is one of the main mechanisms of hydrocarbon recovery in the water-invaded zone of fractured reservoirs. Most rock types of fractured reservoirs are oil-wet carbonate. Thus, the simulation of spontaneous imbibition becomes more important when an enhanced oil recovery (EOR) method such as modified salinity injection is applied for wettability alteration toward a more water-wet state. Since 1962, many scaling equations have been proposed to develop transfer functions and improve the simulation of spontaneous imbibition. Unfortunately, the majority of those proposed scaling equations are developed based on ideal conditions in the laboratory and, hence, have not considered the real conditions of a fractured reservoir. High temperature, rock oil-wettability, live oil properties, wettability alteration, lithology specification, and gravity force are among the important factors ignored in most of the developed scaling equations in the literature. The neglection of these effective elements can cause over- or under-estimation of hydrocarbon recovery by spontaneous imbibition. Therefore, this review discusses the advantages and limitations of scaling equations in the literature with a critical point of view and recommends many research topics for researchers interested in experimental works or data analysis in the  area of spontaneous imbibition. The ideal goal of decades of research in this area is to reach a general scaling equation with a special term for each specific lithology and EOR strategy.

Abstract Machine learning (ML) is emerging as a transformative force in reservoir engineering (RE), addressing long-standing challenges in hydrocarbon exploration and production with unprecedented speed and accuracy. This study explores the core ML paradigms—supervised learning (SL), unsupervised learning (UL), and reinforcement learning (RL)—applied across eight essential domains: reservoir simulation, pore pressure prediction, history matching, reservoir characterization, acidizing operations, hydraulic fracturing, waterflooding, and CO₂-enhanced oil recovery (EOR). By employing algorithms such as artificial neural networks (ANNs), support vector machines (SVMs), particle swarm optimization (PSO), and long short-term memory (LSTM) networks, ML achieves up to 1,000-fold improvements in computational efficiency and predictive accuracy rates exceeding 90%, with potential to reach 100% under optimal conditions—far surpassing traditional approaches, which often plateau at around 75%. These models integrate diverse, multi-scale data to optimize real-time operations, enhance decision-making, and significantly reduce costs. Moreover, ML contributes to sustainability through energy-efficient computations and carbon sequestration capabilities. While key limitations such as data scarcity and limited interpretability remain, promising innovations—including transfer learning and physics-informed models—are opening new pathways. This paper highlights ML’s unique ability to redefine reservoir engineering for a more intelligent and sustainable energy future.