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The urgent problem to be solved in heavy oil exploitation is to reduce viscosity and improve fluidity. Emulsification and viscosity reduction technology has been paid more and more attention and its developments applied. This paper studied the viscosity reduction performance of three types of viscosity reducers and obtained good results. The viscosity reduction rate, interfacial tension, and emulsification performance of three types of viscosity reducers including anionic sulfonate, non-ionic (polyether and amine oxide), and amphoteric betaine were compared with Daqing crude oil. The results showed that the viscosity reduction rate of petroleum sulfonate and betaine was 75–85%. The viscosity reduction rate increased as viscosity reducer concentration increased. An increase in the oil–water ratio and polymer decreased viscosity reduction. When the concentration of erucamide oxide was 0.2%, the ultra-low interfacial tension was 4.41 × 10−3 mN/m. When the oil–water ratio was 1:1, the maximum water separation rates of five viscosity reducers were different. With an increase in the oil–water ratio, the emulsion changed from o/w emulsion to w/o emulsion, and the stability was better. Erucamide oxide and erucic betaine had good viscosity reduction and emulsification effects on Daqing crude oil. This work can enrich knowledge of the viscosity reduction of heavy oil systems with low relative viscosity and enrich the application of viscosity reducer varieties.

Lipid-based biofuels, such as biodiesel and hydroprocessed esters, are a central part of the global initiative to reduce the environmental impact of the transport sector. The vast majority of production is currently from first-generation feedstocks, such as rapeseed oil, and waste cooking oils. However, the increased exploitation of soybean oil and palm oil has led to vast deforestation, smog emissions and heavily impacted on biodiversity in tropical regions. One promising alternative, potentially capable of meeting future demand sustainably, are oleaginous yeasts. Despite being known about for 143 years, there has been an increasing effort in the last decade to develop a viable industrial system, with currently around 100 research papers published annually. In the academic literature, approximately 160 native yeasts have been reported to produce over 20% of their dry weight in a glyceride-rich oil. The most intensively studied oleaginous yeast have been Cutaneotrichosporon oleaginosus (20% of publications), Rhodotorula toruloides (19%) and Yarrowia lipolytica (19%). Oleaginous yeasts have been primarily grown on single saccharides (60%), hydrolysates (26%) or glycerol (19%), and mainly on the mL scale (66%). Process development and genetic modification (7%) have been applied to alter yeast performance and the lipids, towards the production of biofuels (77%), food/supplements (24%), oleochemicals (19%) or animal feed (3%). Despite over a century of research and the recent application of advanced genetic engineering techniques, the industrial production of an economically viable commodity oil substitute remains elusive. This is mainly due to the estimated high production cost, however, over the course of the twenty-first century where climate change will drastically change global food supply networks and direct governmental action will likely be levied at more destructive crops, yeast lipids offer a flexible platform for localised, sustainable lipid production. Based on data from the large majority of oleaginous yeast academic publications, this review is a guide through the history of oleaginous yeast research, an assessment of the best growth and lipid production achieved to date, the various strategies employed towards industrial production and importantly, a critical discussion about what needs to be built on this huge body of work to make producing a yeast-derived, more sustainable, glyceride oil a commercial reality.

CO[sub.2] flooding technology has been established as a key technique that is both economically viable and environmentally sustainable, achieving enhanced oil recovery (EOR) while advancing CCUS objectives. This study addresses the challenge of optimizing CO[sub.2] flooding well patterns in tight oil reservoirs through a geological–engineering integrated approach. A semi-analytical model incorporating startup pressure gradients and miscible/immiscible two-phase flow was developed to dynamically adjust injection intensity. An effective driving coefficient model considering reservoir heterogeneity and fracture orientation was proposed to determine well pattern boundaries. Field data from Blocks A and B were used to validate the models, with the results indicating optimal injection intensities of 0.39 t/d/m and 0.63 t/d/m, respectively. Numerical simulations confirmed that inverted five-spot patterns with well spacings of 240 m (Block A) and 260 m (Block B) achieved the highest incremental oil production (3621.6 t/well and 4213.1 t/well) while reducing the gas channeling risk by 35–47%. The proposed methodology provides a robust framework for enhancing recovery efficiency in low-permeability reservoirs under varying geological conditions.

Excessive water production in mature heavy oil fields causes incremental costs, energy consumption, and inefficiency. Understanding multiphase flows near the wellbore is an alternative to improve production efficiency. Therefore, this study conducts a series of numerical experiments based on the full set of the Navier-Stokes equations in 3D to simulate multiphase flows in porous media for heavy oil production horizontal wells. The solution given by this advanced mathematical formulation led to the description of the movement of the fluids near the wellbore with unprecedented detail. A sensitivity analysis was conducted on different rock and fluid properties such as permeability and oil viscosity, assuming homogeneous porous media. The influence of these parameters on the prediction of the breakthrough time, aquifer movement, and the severity of water production was noticed. Finally, the numerical model was verified against field data using two approaches. The first one was conducting a history match assuming homogeneous rock properties. In contrast, the second one used heterogeneous rock properties measured from well logging, achieving a lower deviation than field data, about 20%. The homogeneous numerical experiments showed that the breakthrough occurs at the heel with a subsequent crestation along the horizontal well. Moreover, at adverse mobility ratios, excessive water production tends to happen in water connings at the heel with an inflow area less than 1% of the total inflow area of the completion liner. Different aquifer movement dynamics were found for the heterogeneous case, like the breakthrough through multiple locations along the horizontal well. Finally, critical hydraulic data in the well, such as the pressure and velocity profiles, were obtained, which could be used to improve production efficiency. The numerical model presented in this study is proposed as an alternative to conducting subsurface modeling and well designs.

Naturally fractured reservoirs are indescribable systems to characterize and difficult to produce and forecast. For the development of such reservoirs, the role of naturally forming fractures in the different development stages needs to be recognized, especially for the pressure maintenance and enhanced oil recovery stages. Recent development in the field of naturally carbonate fractured aimed at fracture characterization, fracture modeling, and fracture network impact of fracture networks on oil recovery were reviewed. Consequently, fracture identification and characterization played pivotal roles in understanding production mechanisms by integrating multiple geosciences sources and reservoir engineering data. In addition, a realistic fracture modeling approach, such as a hybrid, can provide a more accurate representation of the behavior of the fracture and, hence, a more realistic reservoir model for reservoir production and management. In this respect, the influence of different fracture types present in the reservoir, such as major, medium, minor, and hairline fractures networks, and their orientations were found to have different rules and impacts on oil production in the primary, secondary, and EOR stages. In addition, any simplification or homogenization of the fracture types might end in over or underestimating the oil recovery. Improved fracture network modeling requires numerous considerations, such as data collection, facture characterization, reservoir simulation, model calibration, and model updating based on newly acquired field data are essential for improved fracture network description. Hence, integrating multiple techniques and data sources is recommended for obtaining a reliable reservoir model for optimizing the primary and enhanced oil recovery methods.

Oil and natural gas resources are crucial energy sources formed during the geological and biological evolution of the Earth [...]

The transportation of highly viscous oil surrounded by water annulus has been recognized as a feasible option in terms of low-energy consumption and high efficiency. During the process of heavy oil delivery, the problem of pipe fittings is inevitably encountered, and the most common one is elbow assembly. In this present study, simulations for oil-water core annular flow (CAF) through a 90° elbow pipe were performed by computational fluid dynamics (CFD) based on VOF, standard k-ε, and CSF models. Simulation results were consistent with experimental data, which verifies the validity and practicability of the proposed model. The effects of inlet water fraction, superficial velocities of oil and water, oil properties (density and viscosity), and pipe geometry-related parameters (diameter ratio, wall roughness, and surface wettability) on the hydrodynamic performance and stability characteristics were explored. It is revealed that inlet water fraction, superficial velocities of oil and water, oil properties, and pipe geometric parameters do influence the volume fraction of oil and the stability of the water ring. Furthermore, the oil core may adhere to the downstream of the 90° elbow pipe under certain operational conditions. The results could provide a reference for the design of 90° elbow pipe structures and the optimization of operation parameters.

Ultra-low permeability reservoir is characterized by the complex seepage law, Darcy’s law is not applicable to which and it is difficult to predict vertical well horizontal fracture productivity effectively. In this study, according to the law of equivalent percolation resistance, vertical well fracturing horizontal fracture seepage field is divided into two areas by pseudo-well assumptions: the reservoir external seepage resistance generated outside pseudo-well, where seepage flow is radial flow. Reservoir inner seepage resistance generated in pseudo-well, where seepage flow is vertical linear unidirectional flow. Based on principle of hydroelectricity similarity, the inner with the external seepage resistance were combined in series, the vertical well horizontal fracture productivity prediction model was established, and the productivity influence factors were analyzed by simulation. The results demonstrated that horizontal permeability had a great influence on oil production, and vertical permeability had little influence on the production. The bigger starting pressure gradient existed the critical value, the oil wells production showed a marked decline with the value bigger.

Due to the uncertainty of the subsurface environment and the complexity of parameters, particularly in feature extraction from input data and when seeking to understand bidirectional temporal information, the evaluation and prediction of the rate of penetration (ROP) in real-time drilling operations has remained a long-standing challenge. To address these issues, this study proposes an improved LSTM neural network model for ROP prediction (CBT-LSTM). This model integrates the capability of a two-dimensional convolutional neural network (2D-CNN) for multi-feature extraction, the advantages of bidirectional long short-term memory networks (BiLSTM) for processing bidirectional temporal information, and the dynamic weight adjustment of the time pattern attention mechanism (TPA) for extracting crucial information in BiLSTM, effectively capturing key features in temporal data. Initially, data are denoised using the Savitzky–Golay filter, and five correlation coefficient methods are employed to select input features, with principal component analysis (PCA) used to reduce model complexity. Subsequently, a sliding window approach transforms the time series into a two-dimensional structure to capture dynamic changes, constructing the model input. Finally, the ROP prediction model is established, and search methods are utilized to identify the optimal hyperparameter combinations. Compared with other neural networks, CBT-LSTM demonstrates superior performance metrics, with MAE, MAPE, RMSE, and R2 values of 0.0295, 0.0357, 9.3101%, and 0.9769, respectively, indicating the highest predictive capability. To validate the model’s robustness, noise was introduced into the training data, and results show stable performance. Furthermore, the model’s predictive results for other wells achieved R2 values of 0.95, confirming its strong generalization ability. This method provides a new solution for ROP prediction in real-time drilling operations, assisting drilling engineers in better planning their operations and reducing drilling cycles.

By creating a special edition entitled Fundamentals of Enhanced Oil Recovery, the editors focus on the problem of the global increase in energy demand [...]