This paper comprehensively reviews the application and research progress of CO₂ fracturing fluids in China, highlights the existing issues and puts forward suggestions for future development. Three types of fracturing fluid systems containing CO₂, namely, CO₂ dry fracturing fluid, CO₂ energized fracturing fluid, and CO₂ foam fracturing fluid, are categorized based on the mass ratio and process difference between CO₂, water, and treatment agents. Field applications in China reveal several problem to be resolved: (1) The application scope of CO₂ fracturing fluids is restricted to depleted reservoirs, re-fracturing of old wells, and medium-deep reservoirs with low formation pressure coefficients; (2) different types of CO₂ fracturing fluids require different processes and ground supporting equipment; (3) optimization of CO₂ compatibility, functionality, temperature and salt tolerance, as well as the cost of treatment agents is necessitated; (4) existing CO₂ fracturing fluid system fail to perform well with low friction, low filtration, and high sand-carrying capacity. (5) there lacks a targeted industry standard for evaluation of performance of CO₂ fracturing fluid system and treatment agents. Therefore, in order to meet the goals of CCUS-EOR, CCUS-EGR, or integration of fracturing, displacement and burial by CO₂, efforts should be made in the aspects that followed, including in-depth investigation of the mechanism of CO₂ fracturing fluids, the adaptability and compatibility between existing equipment, different CO₂ fracturing fluid systems and processes, and construction of treatment agents, low-density proppants and high-performance systems of recyclability and industrial-grade. In addition, optimization of CO₂ fracturing fluid system based fracturing design is also crucial taking such related factors such as overall reservoir geological conditions, petrophysical properties, CO₂ transportation, and well site layout into consideration.

The ionic liquid, as a new treatment agent, has been increasingly applied in oil fields due to its strong temperature resistance, good solubility and high surface activity. In this paper, we systematically discuss the action mechanism and application effect of ionic liquids in oilfield chemistry. Ionic liquids can inhibit shale hydration expansion and reduce fluid loss through adsorption and intercalation, inhibit the formation of natural gas hydrate through imidazole five-membered ring structure as a space barrier, reduce viscosity of heavy oil by breaking chemical bonds of heavy oil macromolecules and charge transfer, improve oil displacement efficiency by forming ions pairs with carboxyl groups in crude oil, demulsify by forming channels between dispersed water droplets, acidify the formation by reacting with water to produce acid, interacts with organic material through weak hydrogen bonds and extracts it from oilfield wastewater, desulphurize by inserting sulfide molecules into the “stack” structure and form liquid inclusion complex, inhibit corrosion by forming a protective film on the metal surface. Based on the above aspects, the development direction of ionic liquids is proposed. The application of ionic liquids in oilfield chemistry is still in its infancy. It is urgent to fully explore the application performance of ionic liquids in oilfield chemistry, which also provides theoretical and technical supports for efficient reservoir development.

At the end of Early Cambrian time, the Sichuan basin (South China) was located in a wide carbonate platform, with hundreds of meters of carbonate deposited. The Longwangmiao Formation carbonate in Sichuan basin is partially to completely dolomitized, displaying a mottled texture in the northern area of the exposure. The mottled dolomitic limestone developed parallel to bedding, with shape irregular boundaries with limestone that has not been dolomitized. The mottled dolomite is composed of powder crystalline and finely crystalline dolomite, while the matrix limestone is composed of micritic calcite. the isotopic composition of mottled dolomite (δ¹³C = +0.29‰PDB, δ¹⁸O = −1.15‰PDB) is similar to that of micrite calcite (δ¹³C = −0.49‰PDB, δ¹⁸O = −1.45‰PDB). Both isotopic values and trace element data indicate that the dolomitized fluid is originated from sea water. Some beds contain gypsum pseudomorphs and mud cracks, indicating a shallow and evaporative environment with local high salinity during deposition. Dolomitization likely took place early, in part as a result of sea water salinity concentration. Trace fossils thalassinoides horizontalis, thalassinoides callianassa and planolites developed in the Longwangmiao Formation, and the sharp edges of mottled dolomite are similar to these trace fossils. The beds are intensely bioturbated. In the burrow network, the sediments and burrow fill were coarse and loose with little clay, and it is interpreted here as being easier to be dolomitized than the surrounding sediments. Partial dolomitization is thus interpreted to have occurred in the burrow system, and the degree of dolomitization was related to the degree of bioturbation, which is controlled by the trace-making creatures.

The capacitance-resistance model (CRM) has been a useful physics-based tool for obtaining production forecasts for decades. However, the model's limitations make it difficult to work with real field cases, where a lot of various events happen. Such events often include new well commissioning (NWC). We introduce a workflow that combines CRM concepts and kriging into a single tool to handle these types of events during history matching. Moreover, it can be used for selecting a new well placement during infill drilling. To make the workflow even more versatile, an improved version of CRM was used. It takes into account wells shut-ins and performed workovers by additional adjustment of the model coefficients. By preliminary re-weighing and interpolating these coefficients using kriging, the coefficients for potential wells can be determined. The approach was validated using both synthetic and real datasets, from which the cases of putting new wells into operation were selected. The workflow allows a fast assessment of future well performance with a minimal set of reservoir data. This way, a lot of well placement scenarios can be considered, and the best ones could be chosen for more detailed studies.

Natural gas is easily soluble in oil-based muds (OBM), leading to complex flow behavior in wellbores, especially in horizontal wells. In this study, a new transient flow model considering wellbore-formation coupling and gas solubility on flow behavior is developed to simulate gas kicks during horizontal drilling with OBM. Furthermore, the effect of gas solubility on parameters such as bottom-hole pressure (BHP), gas void fraction and mixture velocity in the flow behavior is analyzed. Finally, several critical factors affecting flow behavior are investigated and compared to gas kicks in water-based muds (WBM) where the effect of solubility is neglected. The results show that the invading gas exists as dissolved gas in the OBM and as free gas in the WBM. Before the gas escapes from the OBM, the pit gain is zero and there is barely any change in the BHP, annulus return flow rate and mixture velocity, which means that detecting gas kicks through these warning signs can be challenging until they get very close to the surface and develop rapidly. However, in WBM drilling, these parameters change quickly with the increasing gas kick time. Additionally, for both cases, the longer the horizontal length and the greater reservoir permeability, the greater the decrease in BHP, and the shorter the time for gas to migrate from the bottom-hole to the wellhead. A larger flow rate contributes to a greater initial BHP and a lesser BHP reduction. This research is of value in characterizing gas kick behavior and identifying novel ways for early gas kick detection during horizontal drilling with OBM.

The main objective of this study is to develop the optimal semi-analytical modeling for the infinite-conductivity horizontal well performance under rectangular bounded reservoir based on a new instantaneous source function. The available semi-analytical infinite-conductivity models (ICMs) for horizontal well under rectangular bounded reservoir in literature were developed by applying superposition of pressures in space (SPS). A new instantaneous source function (i.e., instantaneous uniform-flux segmentary source function under bounded reservoir) is derived to be used instead of SPS to develop the optimal semi-analytical ICM. The new semi-analytical ICM is verified with ICM of Schlumberger [1] and with previous semi-analytical ICMs in terms of bottom hole pressure (BHP) profile and inflow rate distribution along the wellbore. The model is also validated with real horizontal wells in terms of inflow rate distribution along the wellbore. The results show that the developed model gives the optimal semi-analytical modeling for the infinite-conductivity horizontal well performance under rectangular bounded reservoir. Besides that, high computational-efficiency and high-resolution of wellbore discretization have been achieved (i.e., wellbore segment number could be tens of hundreds depending on solution requirement). The results also show that at pseudo-steady state (PSS) flow regime, inflow rate distribution along the wellbore by previous semi-analytical ICMs is stabilized U-shaped as performance of inflow rate distribution at late radial flow regime. Therefore, the previous semi-analytical ICMs are incorrectly modeling inflow rate distribution at PSS flow regime due to the negative influence of applying SPS. The optimal semi-analytical ICM is in a general form and real time domain, and can be applicable for 3D horizontal well and 2D vertical fracture well under infinite and rectangular bounded reservoirs, of uniform-flux and infinite-conductivity wellbore conditions at any time of well life.

References

Hydraulic fracturing is a mainstream technology for unconventional oil and gas reservoirs development all over the world. How to use this technology to achieve high-level oil and gas resource extraction and how to form complex fracture networks as hydrocarbon transportation channels in tight reservoirs, which depends to a large extent on the interaction between hydraulic and pre-existing cracks. For hydraulic fracturing of fractured reservoirs, the impact of natural fractures, perforation direction, stress disturbances, faults and other influencing factors will produce a mixed Ⅰ&Ⅱ mode hydraulic fracture. To forecast whether hydraulic fractures cross pre-existing fractures, according to elastic mechanics and fracture mechanics, a stress state of cracks under the combination of tensile (Ⅰ) and shear (Ⅱ) is presented. A simple mixed-mode Ⅰ&Ⅱ hydraulic fracture's crossing judgment criterion is established, and the propagation of hydraulic fractures after encountering natural fractures is analyzed. The results show that for a given approaching angle there exists a certain range of stress ratio when crossing occurs. Under high approaching angle and large stress ratio, it is likely that hydraulic cracks will go directly through pre-existing cracks. The reinitiated angle is always controlled within the range of approximately 30° among the main direction of penetration.

Oilfield treated oil pipeline network is the link connecting the upstream oilfields and the downstream refineries. Due to the differences in operating costs and transportation fee between different pipelines and the fluctuation in the demand and sales prices of the treated oil, there is an optimal flow allocation plan for the pipeline network to make the oilfield company obtain the highest social and economic benefit. In this study, a mixed integer nonlinear programming (MINLP) model is developed to determine the optimal flow rate allocation plan of the large-scale and complex treated oil pipeline network, and both the social and economic benefits are considered simultaneously. The optimization objective is the multi-objective which includes the largest user satisfaction and the highest economic benefit. The model constraints include the oilfield production capacity, refinery demand, pipeline transmission capacity, flow, pressure, and temperature of the node and station, and the pipeline hydraulic and thermal calculations. Python 3.7 is utilized for the programming of the off-line calculation procedure and the MINLP model, and GUROBI 9.0.2 is served as the MINLP solver. Moreover, the model is applied to a real treated oil pipeline network located in China, and three optimization scenarios are analyzed. For social benefit, the values of the user satisfaction of each refinery and the total network are 1 before and after optimization for scenarios 1, 2, and 3. For economic benefit, the annual revenue can be increased by 0.227, 0.293, and 0.548 billion yuan after the optimization in scenario 1, 2, and 3, respectively.

Miscible natural gas injection is widely considered as a practical and efficient enhanced oil recovery technique. However, the main challenge in this process is the high minimum miscibility pressure (MMP) between natural gas and crude oil, which limits its application and recovery factor, especially in high-temperature reservoirs. Therefore, we present a novel investigation to quantify the effect of chemical-assisted MMP reduction on the oil recovery factor. Firstly, we measured the interfacial tension (IFT) of the methane-oil system in the presence of chemical or CO₂ to calculate the MMP reduction at a constant temperature (373K) using the vanishing interfacial tension (VIT) method. Afterwards, we performed three coreflooding experiments to quantify the effect of MMP reduction on the oil recovery factor under different injection scenarios.

The interfacial tension measurements show that adding a small fraction (1.5 wt%) of the tested surfactant (SOLOTERRA ME-6) achieved 9% of MMP reduction, while adding 20 wt% of CO₂ to the methane yields 13% of MMP reduction. Then, the coreflooding results highlight the significance of achieving miscibility during gas injection, as the ultimate recovery factor increased from 65.5% under immiscible conditions to 77.2% using chemical-assisted methane, and to 79% using gas mixture after achieving near miscible condition. The results demonstrate the promising potential of the MMP reduction to significantly increase the oil recovery factor during gas injection. Furthermore, these results will likely expand the application envelop of the miscible gas injection, in addition to the environmental benefits of utilizing the produced gas by re-injection/recycling instead of flaring which contributes to reducing the greenhouse gas emissions.

The research focuses on evaluating how well new solvents attract light hydrocarbons, such as propane, methane, and ethane, in natural gas sweetening units. It is important to accurately determine the solubility of hydrocarbons in these solvents to effectively manage the sweetening process. To address this challenge, the study proposes using advanced empirical models based on artificial intelligence techniques like Multi-Layer Artificial Neural Network (ML-ANN), Support Vector Machines (SVM), and Least Square Support Vector Machine (LSSVM). The parameters for the SVM and LSSVM models are estimated using optimization methods like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Shuffled Complex Evolution (SCE). Data on the solubility of propane, methane, and ethane in various ionic liquids are collected from reliable literature sources to create a comprehensive database. The proposed artificial intelligence models show great accuracy in predicting hydrocarbon solubility in ionic liquids. Among these, the hybrid SVM models perform exceptionally well, with the PSO-SVM hybrid model being particularly efficient computationally. To ensure a comprehensive analysis, different examples of hydrocarbons and their order are included. Additionally, a comparative analysis is conducted to compare the AI models with the thermodynamic COSMO-RS model for solubility analysis. The results demonstrate the superiority of the AI models, as they outperform traditional thermodynamic models across a wide range of data. In conclusion, this study introduces advanced artificial intelligence algorithms such as ML-ANN, SVM, and LSSVM in accurately estimating the solubility of hydrocarbons in ionic liquids. The incorporation of optimization techniques and variations in hydrocarbon examples improves the accuracy, precision, and reliability of these intelligent models. These findings highlight the significant potential of AI-based approaches in solubility analysis and emphasize their superiority over traditional thermodynamic models.

The technique of Enhanced Gas Recovery by CO₂ injection (CO₂-EGR) into shale reservoirs has brought increasing attention in the recent decade. CO₂-EGR is a complex geophysical process that is controlled by several parameters of shale properties and engineering design. Nevertheless, more challenges arise when simulating and predicting CO₂/CH₄ displacement within the complex pore systems of shales. Therefore, the petroleum industry is in need of developing a cost-effective tool/approach to evaluate the potential of applying CO₂ injection to shale reservoirs. In recent years, machine learning applications have gained enormous interest due to their high-speed performance in handling complex data and efficiently solving practical problems. Thus, this work proposes a solution by developing a supervised machine learning (ML) based model to preliminary evaluate CO₂-EGR efficiency. Data used for this work was drawn across a wide range of simulation sensitivity studies and experimental investigations. In this work, linear regression and artificial neural networks (ANNs) implementations were considered for predicting the incremental enhanced CH₄. Based on the model performance in training and validation sets, our accuracy comparison showed that (ANNs) algorithms gave 15% higher accuracy in predicting the enhanced CH₄ compared to the linear regression model. To ensure the model is more generalizable, the size of hidden layers of ANNs was adjusted to improve the generalization ability of ANNs model. Among ANNs models presented, ANNs of 100 hidden layer size gave the best predictive performance with the coefficient of determination (R²) of 0.78 compared to the linear regression model with R² of 0.68. Our developed ML-based model presents a powerful, reliable and cost-effective tool which can accurately predict the incremental enhanced CH₄ by CO₂ injection in shale gas reservoirs.

Reservoir simulation is known as perhaps the most widely used, accurate, and reliable method for field development in the petroleum industry. An integral part of a reliable reservoir simulation process is to consider robust and rigorous tuned EOS models. Traditionally, EOS models are tuned iteratively through arduous workflows against experimental PVT data. However, this comes with a number of drawbacks such as forcingly using weight factors, which upon alteration adversely affects the optimization process. The objective of the current work is thus to introduce an auto-tune PVT matching tool using NSGA-II multi-objective optimization. In order to illustrate the robustness of the presented technique, three different PVT samples are used, including two black-oil and one gas condensate sample. We utilize Peng-Robinson EOS during all the manual and auto-tuning processes. Comparison of auto-tuned EOS-generated results with those of experimental and computed statistical error values for these samples clearly show that the proposed method is robust. In addition, the proposed method, contrary to the manual matching process, provides the engineer with several matched solutions, which allows them to select a match based on the engineering background to be best amenable to the problem at hand. In addition, the proposed technique is fast, and can output several solutions within less time compared to the traditional manual matching method.

Rock samples' TOC content is the best indicator of the organic matter in source rocks. The origin rock samples’ analysis is used to calculate it manually by specialists. This method requires time and resources because it relies on samples from many well intervals in source rocks. Therefore, research has been done to aid this effort. Machine learning algorithms can estimate total organic carbon instead of well logs and stratigraphic studies. In light of these efforts, the current work present a study on automating the total organic carbon estimation using machine learning approaches improved by an evolutionary methodology to give the model flexibility and precision. Genetic algorithms, differential evolution, particle swarm optimization, grey wolf optimization, artificial bee colony, and evolution strategies were used to improve machine learning models to predict TOC. The six metaheuristics were integrated into four machine learning methods: extreme learning machine, elastic net linear model, linear support vector regression, and multivariate adaptive regression splines. Core samples from the YuDong-Nan shale gas field, located in the Sichuan basin, were used to evaluate the hybrid strategy. The findings show that combining machine learning models with an evolutionary algorithms in a hybrid fashion produce flexible models that accurately predict TOC. The results show that, independent of the metaheuristic used to guide the model selection, optimized extreme learning machines attained the best performance scores according to six metrics. Such hybrid models can be used in exploratory geological research, particularly for unconventional oil and gas resources.

As an effective method to prolong the life of mature field, conformance control in water-injection well has been used wildly. Naturally, effect evaluation of conformance control has attracted great attention because it is an important guideline for the design of later enhanced oil recovery (EOR) plan. Usually, production responses such as excessive water reduction and oil production increment are widely used as the indicators. However, production responses may be unreliable due to the difficulty in determining an effective injection well which is caused by a large number of treated water-injection wells in a well group. Therefore, with the application of fuzzy comprehension evaluation (FCE), five evaluation indexes (injection pressure, injectivity index, slope of hall curve, variation coefficient and homogenization coefficient of injection profile) describe injection responses were selected to establish a new evaluation method in this paper. Based on fuzzy mathematics, FCE reflects the difference of evaluation units. Meanwhile, weights of evaluation indexes were obtained by analytic hierarchy process (AHP), which made the results more convincing. Taking Bai 239 oilfield as an example, the five injection responses indexes were used to assess treatment effect on five water-injection wells by single index evaluation and FCE. The results showed that among the five evaluation indexes mentioned above, the slope of hall curve was the most important factor affected evaluation result. In single index evaluation, opposite results may be produced easily on account of the one-sidedness of single index or human error. Furthermore, we found that effective treatment was a relative concept actually. The result of FCE was consistent with single index evaluation but FCE was more acceptable. This study suggests that FCE could be applied to another field such as water flooding, acidizing and hydraulic fracturing