Petroleum最新文献

The present work investigates the volumetric and viscometric properties of an aqueous solution of 1,2-dimethylethylenediamine (DEEDA) over an entire concentration range and an absorber operating temperature range of 313.15K–333.15K at atmospheric pressure. The investigated volumetric properties included the density, excess molar volume, partial molar volume, and the investigated viscometric properties included the viscosity, viscosity deviation, free energy for activation of viscous flow, excess free energy for activation of viscous flow, and excess entropy for activation of viscous flow. The results indicated that there are strong intermolecular interactions and suitable molecular packing in the binary DEEDA–water mixture. Hence, the mixture was found to deviate from a real mixture according to the calculated excess properties. The DEEDA solvent's preliminary volumetric and viscometric properties revealed convincing potential as a novel amine for carbon capture. Additionally, the Redlich-Kister-based correlations showed favorable correlative performance for excess molar volume, viscosity deviation, and excess entropy for activation of viscous flow.

Graphene is a single atom thick crystal composed of carbon atoms. It is the lightest, thinnest, strongest material that conducts heat and electricity well heretofore. In terms of application, by introducing oxygen-containing groups, graphene can be well dispersed in solvents, can be chemically modified and functionalized, or connected with other electroactive substances through covalent bond or non-covalent bond to form composite materials, which is conducive to further processing and promotion. The application of graphene in oilfield chemistry started late, but developed rapidly. Graphene has played an active role in drilling fluid, cementing fluid, fracturing fluid, displacement fluid and other oilfield working fluids. It can enhance the temperature and salt resistance of working fluid and improve the effect of working fluid. In this paper, several directions of graphene applications in oilfield chemistry, such as modified graphene, graphene copolymers and graphene nanoparticles, are reviewed in detail from the synthesis methods, action mechanisms and effects of graphene and its derivatives, and the frontier cases at this stage are given. On the basis of the existing research, suggestions for the development direction of graphene materials in oilfield chemistry are given for a variety of graphene materials, aiming to provide guidance for the application of graphene in oilfield chemistry.

Due to the extremely low permeability of shale formations, the combination of horizontal well and volume fracturing has been proposed as an effective technique to improve the production of Dagang continental shale oil reservoirs. Based on the flow material balance method (FMB) and straight-line analysis (SLA) method, the stimulated reservoir volume (SRV) and drainage volume are determined to identify the flow regimes of the seepage mechanism of shale oil reservoirs. To resolve the challenges of multi-scaled flow regimes and bottom hole pressure (BHP) variation before and after pumping in shale oil wells, a multi-linear analytical flow model was established to predict the future production and the final expected ultimate recoverable oil (EUR_o) after fitting the historical production dynamics. Based on the results, it can be concluded that the flow regime of a shale oil well in production can be divided into two stages consisting of linear flow within SRV and composite flow from the un-stimulated area to SRV. The effects of fracturing operation parameters, such as fracturing fluid volume and sand/liquid ratio, on shale oil productivity, are analyzed, and insightful suggestions are drawn for the future development of this pay zone.

Increasing world request for energy has made oil extraction from reservoirs more desirable. Many novel EOR methods have been proposed and utilized for this purpose. Using nanocomposites in chemical flooding is one of these novel methods. In this study, we investigated the impact of six injection solutions on the recovery of light and heavy oil with the presence of two different brines as formation water using a homogenous glass micromodel. All of the injection solutions were based on a 40,000 ppm NaCl synthetic seawater (SSW), one of which was additive free and the others were prepared by dispersing nanocomposite silica-based polyacrylamide (NCSP), nanocomposite alumina-based polyacrylamide (NCAP), the combination of both nanocomposites silica and alumina based on polyacrylamide (NCSAP), surfactant (CTAB) and polyacrylamide (PAM) with a concentration of 1000 ppm as additives. The Stability of nanocomposites was tested against the salinity of the brine and temperature using salinity and DSC tests which were successful. Alongside stability tests, IFT, contact angle and oil recovery measurements were made. Visual results revealed that in addition to the effect of silica and alumina nanocomposite in reducing interfacial tension and wettability alteration, control of mobility ratio caused a major improvement in sweeping efficiency and oil recovery. According to the sweeping behavior of injected fluids, it was found that the main effect of surfactant was wettability alteration, for polyacrylamide was mobility control and for nanocomposites was the reduction of interfacial tension between oil and injected fluid, which was completely analyzed and checked out. Also, NCSAP with 95.83% and 70.33% and CTAB with 84.35% and 91% have the highest light oil recoveries at 250,000 ppm and 180,000 ppm salinity, respectively which is related to the superposition effect of interactions between nanocomposites, solution and oil. Based on our results it can be concluded that the most effective mechanism in oil recovery was IFT reduction which was done by CTAB reduction also by using a polymer-based nanocomposite such as NCSAP and adding the mobility control factor, the oil recovery can be further enhanced. In the case of heavy oil recovery, it can be concluded that the mobility control played a much more effective role when the PAM performed almost similarly to the CTAB and other nanocomposites with a recovery factor of around 17%. In this study, we tried to investigate the effect of different injection solutions and their related mechanisms on oil recovery.

Fluid production from unconsolidated reservoirs often leads in sand production, which poses a number of issues. Sand deposition in flowlines can result in significant pressure dips, pipe and facility damage, and obstructions that decrease productivity. More research is needed to understand the movement and deposition of sand in oil–water–sand (O–W–S) fluxes. This article focuses on O–W–S flows in a 6-meter-long horizontal pipe with an inner diameter of 0.0381 m. The study looks at the flow behavior of high viscosity oil–water (O–W), water–sand (W–S), and oil–water–sand (O–W–S) flows. Experiments were carried out at 250 psig pressure in a laboratory flow test facility using various heavy synthetic oils (viscosities ranging from 3500 cP to 7500 cP at 25°C) and tap water. The sand concentration varied from 1% to 10%, with an average sand particle diameter of 145 μm and material density of 2630 kg/m³. Water cuts ranged from 0.0 to 1.0. The experimental results revealed a minor change in pressure gradient between (O–W) and (O–W–S) flows. However, increasing the sand concentration in (O–W–S) flow resulted in higher pressure losses. The reduction factor of pressure gradient indicated that the highest decrease in pressure drop occurred at higher superficial oil velocities. Furthermore, a direct relationship was observed between the reduction factor and the decrease in water cut. The results also showed that the minimum required transportation velocity for sand slurry decreased with increasing superficial oil velocity, while the minimum transportation condition increased with higher sand concentration. The comparison between the expected pressure gradient from Bannwart and McKibben et al. and the actual experimental data demonstrated significant accuracy for the oil viscosities and superficial oil velocities used in the study.

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 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.

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.

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.

Drilling vibrations significantly impact drilling operations with high costs due to early downhole equipment failure and loss of productive time. Stick-slip vibrations, a severe form of torsional vibrations, is known to be present up to 50% of total drilling time, making it a topic of immense concern and research. An ongoing discussion in the industry is regarding the reliability of surface measurements for early detection of severe downhole bit sticking. Moreover, most surface measurements are sampled at lower frequency rates closer to 1 Hz. Recently, the implementation of advanced data acquisition modules in downhole subs has greatly improved our understanding of drilling vibrations through high resolution data, sampled up to 10 kHz. However, with a wide range of sampling frequency to choose from different available tools, a critical question remains unanswered. What is an optimal and adequate sampling frequency for early detection of downhole vibrations using both surface and downhole measurements? The paper addresses the question with a focus on stick-slip vibrations through an experimental investigation. Stick slip tests are repeated for different sampling frequencies of surface and downhole measurements and the stick slip index for each case is calculated. The stick-slip index varies for different sampling frequency even though the vibration tests remain completely identical. It was inferred that sampling frequency of measurements greatly impact the detection of downhole vibrations. Even though stick-slip vibrations are characteristically low frequency vibrations (≤2Hz), a minimum of 10Hz sampling frequency is recommended for detection of stick-slip vibrations. Moreover, all characteristics of stick-slip vibrations including bit sticking, bit RPM peaks and negative bit RPMs are clearly observed at a minimum of 100Hz sampling rate.