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Nanofluid-enhanced oil recovery (EOR) technology is an innovative approach to enhancing oil production in oilfields. It entails the dispersion of nanoparticles within a fluid, strategically utilizing the distinctive properties of these nanoparticles (NPs) to engage with reservoir rocks or crude oil, resulting in a significant enhancement of the oil recovery rate. Despite the notable advantages of nanofluid EOR technology over conventional oil recovery methods such as binary and ternary flooding, practical implementations continue to grapple with a range of pressing challenges. These challenges encompass concerns regarding the economic viability, stability, and adaptability of nanomaterials, which pose significant barriers to the widespread adoption of nanofluid EOR technology in the oil field. To tackle these challenges, addressing the current issues may involve selecting simpler and more readily available materials coupled with straightforward material modification techniques. This approach aims to more effectively meet the requirements of large-scale on-site applications. Within this framework, this review systematically explores commonly employed nanofluids in recent years, including inorganic nanofluids, organic nanofluids, and composite nanofluids. It categorizes the research advancements in optimizing modification techniques and provides a comprehensive overview of the mechanisms that underpin nanofluid EOR technology and its practical applications in oilfields. This comprehensive review aims to offer valuable references and serve as a solid foundation for subsequent research endeavors.

Emulsification plays an important role in enhancing oil recovery. Experiments and field applications of alkali/surfactant/polymer (ASP) flooding indicated that the amount of oil recovery in liquids with emulsions is 5% higher than that in liquids with no emulsions. Therefore, it is of great significance to study emulsion and its field application for enhanced oil recovery. This paper discusses the current status of emulsion for enhanced oil recovery, including the formation mechanism of emulsions in chemical flooding, rheological properties, stability, seepage characteristics, emulsion improving sweep volume, and displacement efficiency, along with future development plans of emulsion for enhanced oil recovery, especially surfactants for chemical flooding. In addition, the Pickering emulsion for application in enhanced oil recovery is also discussed. The development effects of emulsion flooding have been discussed for the Midway‐Sunset Oilfield, the emulsification characteristics of ASP flooding have been analyzed in Xing‐V and Xing‐II of the Daqing Oilfield, and the experiences regarding emulsion for enhanced oil recovery have been summarized. The key research directions of emulsion for enhanced oil recovery are indicated. Emulsification plays an important role in enhancing oil recovery. Although there have been developments, some fundamental aspects of emulsion seepage and the mechanism of emulsion enhancing oil recovery remain unclear. The emulsion formation mechanism, rheological properties, stability, seepage characteristics, and enhanced oil recovery are discussed in detail. The application of emulsion for enhanced oil recovery is analyzed, and the experiences from laboratory tests and field tests are summarized.

The Yangsanmu oilfield of Dagang is a typical heavy oil reservoir. After the maximum primary production (waterflooding), more than half of the original oil is still retained in the formation. Therefore, the implementation of an enhanced oil recovery (EOR) process to further raise the production scheme is inevitable. In this work, a novel in-situ CO 2 foam technique which can be used as a potential EOR technique in this oilfield was studied. A screening of gas producers, foam stabilizers and foaming agents was followed by the study of the properties of the in-situ CO 2 foam systems through static experiments. Core-flooding experiments and field application were also conducted to evaluate the feasibility of this technique. The results indicated that the in-situ CO 2 foam system can improve both the sweep and displacement efficiencies, due to the capacity of this system in reducing oil viscosity and interfacial tension, respectively. The EOR performance of the in-situ CO 2 foam system is better than the single-agent and even binary system (surfactant-polymer) flooding. The filed data demonstrated that the in-situ CO 2 technique can significantly promote oil production and control water cuts. These results are believed to be beneficial in making EOR strategies for similar reservoirs.

A kind of second-order implicit upwind fractional step finite difference methods are presented for the numerical simulation of coupled systems for enhanced (chemical) oil production with capillary force in the porous media. Some techniques, e.g., the calculus of variations, the energy analysis method, the commutativity of the products of difference operators, the decomposition of high-order difference operators, and the theory of a priori estimate, are introduced. An optimal order error estimate in the l2 norm is derived. The method is successfully used in the numerical simulation of the enhanced oil production in actual oilfields. The simulation results are satisfactory and interesting.

The concepts of inspection, monitoring, and testing often overlap, and many organizations have slightly different definitions of the terms. Inspections can be planned and scheduled or occur during unplanned shutdowns, construction modifications, etc. This chapter covers some of the nondestructive testing (NDT) inspection methods most likely to be used in upstream oilfield operations. Most monitoring techniques require the insertion of metal samples of some type into corrosive production fluids. Electrochemical corrosion monitoring techniques include: Linear polarization resistance (LPR), Tafel extrapolation, Galvanic monitoring, Electrochemical noise, and AC impedance spectroscopy. Testing is used in two senses in oilfield applications. Hydrostatic testing is commonly used to “proof” newly constructed or altered equipment to ensure that the equipment will be safe to operate under the intended temperature and pressure conditions. The other use of the term is for relatively short‐term laboratory or field trials to determine materials compatibility, the effectiveness or corrosion inhibitors, etc.

Daqing oilfield chemical flooding technology is mature and matching, enhanced oil recovery, strongly support the development of oil fields, in order to ensure the national strategic security of energy plays a pivotal role. Since the 13th five-year plan, aiming at the problems of poor quality reservoir condition, poor development benefit and increasing difficulty of stable production, the target of high-efficiency development has been set, innovative Management mode, the construction of “Key research, continuous improvement, promotion and application” closely linked to the new mechanism of integrated scientific research and production management; A new dynamic management mechanism of chemical flooding development is established, and a new management model of injection system is established. Since 2016, the development efficiency of chemical flooding has improved markedly, with annual oil production remaining stable at over 10 million tons and an additional 2.55 million tons of oil production. The tonnage of polymer-enhanced oil has increased from 47.4 tons to 57.2 tons, saving 126,000 tons of polymer and generating 5.503 billion yuan in economic benefits, Daqing Field has made important contributions to the revitalization and development of the country in the new era and ensuring national energy security.

Intelligent oilfield development is experiencing an increasingly deep integration between localized automation and integrated, data-centric ecosystems. To systematically delineate the knowledge structure and technological trajectories within this field, this study analyzes 225 high-quality publications. This study innovatively employs a custom toolchain based on the Dart language for heterogeneous data cleaning and standardization, ensuring high accuracy and scientific rigor in the analysis samples. The investigation reveals a distinct dual-core driving mechanism underpinning recent advancements: a cognitive cluster centered on Artificial Intelligence and Deep Learning for complex data interpretation and prediction, and a decision-making cluster focused on Operational Optimization and Predictive Modeling for production enhancement. These two clusters respectively encompass eight sub-clusters: “artificial intelligence,” “machine learning,” “deep learning,” “performance,” “enhanced oil recovery,” “model,” “optimization,” and “predication.” This dual-core framework signifies a paradigm shift from experience-based practices to a synergistic “AI-enabled + mathematical optimization” approach. The analysis further explores emerging trends, including the potential of deep reinforcement learning for dynamic decision-making and the critical role of cybersecurity and model robustness in safety risk management. By mapping the current landscape and core mechanisms, this study provides a foundational reference for researchers and practitioners to navigate the future development of intelligent oilfields towards more resilient and efficient ecosystems.

In this work, a multi-period nonlinear programming formulation is presented to obtain the optimal long-term oilfield production planning, based on a two-phase, one-dimensional, and Cartesian-coordinated phenomenological reservoir model. The phenomenological model contains a set of second-order partial differential equations, which are approximated by a collocation on finite element method. This CFE method prevents mathematical stability limitations due to stiffness problems, resulting in an algebraic equation system added as an optimization set of constraints. This is a significant and innovating approach as there are only a handful of similar studies in the literature that integrate phenomenological models as mathematical constraints in the optimization problem. However, these works do not solve the model using long-term production planning coupled with a simultaneous strategy. Also, formulation applied to two study cases allowed solving the optimization problem within an adequate time without requiring a high-performance computing platform. Results show the economic impact of simultaneously considering the constraints and the state variables evolution throughout the reservoir’s life span to obtain the optimal long-term production planning.

Fractal theory of particle size distribution (PSD) is a widely used approach in soil science. However, fractal studies on sandstone PSDs are scarce in sedimentology and geology. Taking littoral sandstones in the Carboniferous Donghetang Formation of the Hade Oilfield as an example, fractal dimensions of 115 fine sandstone and 150 silty sandstone PSDs are calculated and compared with particle size compositions and traditional statistical parameters in this paper. The results show that fractal dimension values in fine sandstones, 1.69–2.17 averaged at 1.99, are usually lower than that in silty sandstones, 2.12–2.73 averaged at 2.37. Fractal dimension and sandy content of littoral sandstones show a strong negative linear relationship. Significant logarithmic correlations are implied between fractal dimension and silty and clayey contents of littoral sandstones, which is different from linear relations in soil PSDs. The relationships between fractal dimension and mean, sorting, and skewness of silty sandstone PSDs are better than those of fine sandstones. Fractal dimension and kurtosis of silty sandstones and fine sandstones exhibit weak negative and positive linear relationships, respectively. Fractal dimension values in lower-shoreface facies, 2.05–2.47 averaged at 2.33, are generally higher than that in upper-shoreface facies, 1.79–2.30 averaged at 2.11. Fractal dimension values in bar and beach microfacies are commonly lower than those in trough microfacies. Combined with additional sedimentary information from various clastic deposits, the fractal dimension can serve as a new depositional environment indicator.

The driving technology of tertiary oil recovery is a higher-level oil recovery technology based on primary and secondary oil recovery technology, which is an important technology to fully exploit oil resources. Compared with the secondary oil recovery technology, the tertiary oil recovery and displacement technology is a great leap in the field of technology, which will greatly improve the efficiency of oil recovery and displacement. Tertiary oil recovery technology is based on the dual effect of physical reaction and chemical reaction, which will better exploit oil and natural resources. At present, the EOR technology has been applied to many large oil fields, which has become a perfect production technology. Through the tertiary oil recovery technology, the production efficiency of the oilfield has been improved by one third. Firstly, this paper analyzes the working principle of tertiary oil recovery technology. Then, this paper analyzes the application of EOR technology.