Petroleum Research最新文献

Chemical enhanced oil recovery (cEOR) remains one of the most potent tertiary recovery techniques. However, it is expensive and rarely environmentally friendly. Bio-based amphiphilic polymers have been suggested as an alternative to eradicating the challenges of conventional cEOR because of cost-effectiveness, and sustainability. Unfortunately, few in-depth studies exist in the literature to investigate the prospects of these materials. A new family of amphiphilic polysaccharides was synthesized by hydrophobic modification of cellulose sulphate, and the EOR functionalities were tested. The novel biopolymers exhibited the ability to alter rock wetting properties. In terms of recovery, one of the variants of the synthesized bio amphiphilic polymer (D-I) was able to reduce residual oil saturation to 12% at harsh conditions of 60,000 ppm salinity at 75 °C. Micromodel visual analysis revealed that the performance of the novel materials was due to the combination of mobility control, IFT lowering and emulsification, wettability alteration, and viscoelasticity. With a performance commensurate to that of a commercial hydrophobically modified polymer, it can be said that the novel amphiphilic polysaccharides can stand as a viable cEOR agent for oilfield applications.

Sedimentary facies identification is critical for carbonate oil and gas reservoir development. The traditional method of sedimentary facies identification not only be affected by the engineer's experience but also takes a long time. Identifying carbonate sedimentary facies based on machine learning is the trend of future development and has the advantages of short time consuming and reliable results without engineers' subjective influence. Although many references reported the application of machine learning to identify lithofacies, but identifying sedimentary facies of carbonate reservoirs is much more challenging due to the complex sedimentary environment and tectonic movement. This paper compares the performance of the carbonate sedimentary facies identification using four different machine learning models, and the optimal machine learning with the highest prediction accuracy is recommended. First, the carbonate sedimentary facies are classified into the lagoon, shallow sea, shoal, fore-shoal, and inter-shoal five tags based on the well loggings. Then, five well log curves including spectral gamma ray (SGR), uranium-free gamma ray (CGR), photoelectric absorption cross-section index (PE), true formation resistivity (RT), shallow lateral resistivity (RS) are used as the input, and the manual identified carbonate sedimentary facies are used as the output of the machine learning model. The performance of four different machine learning algorithms, including support vector machine (SVM), deep neural network (DNN), long short-term memory (LSTM) network, and random forest (RF) are compared. The other two wells are used for model validation. The research results show that the RF method has the highest accuracy of sedimentary facies prediction, and the average prediction accuracy is 78.81%; the average accuracy of sedimentary facies prediction using SVM is 77.93%. The sedimentary facies predictions using DNN and LSTM are less satisfying compared with RF and SVM, and the average accuracy is 69.94% and 73.05%, respectively. The predicted carbonate sedimentary facies by LSTM are more continuous compared with other machine learning models. This study is helpful for identifying compelx sedimentary facies of carbonate reservoirs from well logs.

The research objectives are to assess the possibility of using drill cutting analysis to obtain information about the mineralogical and geochemical properties of the reservoir rocks. Drill cutting samples were collected from a vertical well that penetrated the Domanik sediments (Semiluksk Formation) in one of the oil fields in the Volga-Ural petroleum province. Thin sections from drill cuttings were examined using an optical polarizing microscope (Axio Imager A2). X-ray diffraction (XRD) analyses were performed using a Brucker D2 Phaser X-ray powder diffractometer. Thermophysical properties were studied using an STA 449 F3 Jupiter instrument. The pyrolytic studies were performed using the Rock-Eval method. Visual inspection showed that the studied sediments are alternations of carbonates and siliceous-carbonate rocks. Thin section examinations revealed that the carbonates are mainly limestone (mudstone and wackestone) and are characterized by a dense texture and up to 30 % organic residues. The siliceous-carbonate rocks are dominated by siliceous mudstones and are characterized by dark colours, layered structure, and an enrichment in organic matter. XRD analyses showed that the carbonate rocks are mainly composed of calcite, dolomite, quartz, feldspar, and mica, which are minor components. The siliceous-carbonate rocks are dominated by quartz, followed by calcite, although they also contain feldspars, mica, dolomite, and pyrite as impurities. According to the simultaneous thermal analysis, the average total hydrocarbon in the carbonate and siliceous-carbonate rocks is 13.6 % (for the core samples) and 11.5 % (for the drill cutting samples). The content of heavy hydrocarbons in the rocks is higher than the content of light hydrocarbons, indicating the immature nature of organic matter. Kerogen is found sporadically in siliceous-carbonate rocks. According to the pyrolytic studies, average S1 is 4.4 mg/g and average S2 is 19.8 mg/g (for the core samples); average S1 is 2.1 mg/g and average S2 is 17.8 mg/g (for the drill cutting samples), which indicated that the studied sediments have very good to excellent generation potential. The average T_max of 425.7 °C (for the drill cutting samples) and 427.9 °C (for the core samples) indicate immature organic matter that generated only heavy oils. Comparing the results of the analysed drill cutting samples with the results of the analysed core analysis from the same reservoir interval in the neighbouring wells showed a good correlation, which proves that this technique is a valid tool that provides an alternative, cost-effective method to determine the rock's characteristics from drill cuttings.

Plugging and abandonment (P&A) is a crucial step of the well life cycle. Regardless of how long one stretches the productive life of a well, P&A operations will have to be carried out eventually. The current panorama of our industry includes many wells to be plugged and abandoned, with steep requirements to abide by both regulations and societal pressure. In this context, we must guarantee that no leakage occurs with an eternal perspective in mind. Cement has been the prime material for this task, but recent studies have indicated the potential of degradation over time – especially in corrosive environments – and the creation of leaking paths due to its shrinkage. This has opened up a path toward the usage of alternative materials. One of the emerging candidates is bismuth, a metal with the unique characteristic of expanding when solidified. Such a trait could improve the overall sealability of wellbores and especially during P&A. This article discusses the current status of bismuth sealing technologies, introducing the basics of bismuth, the ongoing efforts to qualify it as a barrier material, its potential applications, and the challenges that still need to be overcome. The latest research indicates promising results in terms of its usage as a barrier element.

The aim of this study is to evaluate the internal corrosion process on X52 and X80 steels/real petroleum interfaces containing condensed hydrocarbon plus oilfield-produced water, which were subjected to stimulated emulsions using 50/50 vol ratio mixtures at 45 °C, different hydrodynamic conditions, 1 h, and 24 h. A washing process by using deionized water was proposed to simulate and identify the corrosiveness of the hydrocarbon phase after 24 h of exposure time. The characterization by electrochemical impedance spectroscopy and the monitoring of the polarization curves indicated that X80 steel/oilfield-produced water interfaces were more susceptible to corrosion than X52 steel exposed to oilfield-produced water. The combined speed rotation of 600 rpm using a magnetic stirrer + 600 rpm using a rotating disk electrode decreased the corrosion rate on X52 steel. The stimulated emulsions made of hydrocarbon + oilfield-produced water and hydrocarbon + deionized water at 24 h increased the corrosion rate on X80 steel (0.34 mm/year and 0.43 mm/year, respectively), promoting the formation of erosion and pitting corrosion. These types of corrosion depended mainly on the physicochemical properties of the hydrocarbon, oilfield-produced water, exposure times, and hydrodynamic systems in which the hydrocarbon was studied.

In the petroleum industry, the analysis of petrophysical parameters is critical for efficient reservoir management, production optimization, development strategies, and accurate hydrocarbon reserve estimations. Over recent years, the integration of machine learning methodologies has revolutionized the field, addressing challenges in geology, geophysics, and petroleum engineering, even when confronted with limited or imperfect data. This study focuses on the prediction of density logs, a pivotal factor in evaluating reservoir hydrocarbon volumes. It is important to note that during well logging operations, log data for specific depths of interest may be missing or incorrect, presenting a significant challenge. To tackle this issue, we employed the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Networks (ANN) in combination with advanced optimization algorithms, including Particle Swarm Optimization (PSO), Imperialist Competitive Algorithms (ICA), and Genetic Algorithms (GA). These methods exhibit promising performance in predicting density logs from gamma-ray, neutron, sonic, and photoelectric log data. Remarkably, our results highlight that the Genetic Algorithms-based Artificial Neural Network (GA-ANN) approach outperforms all other methods, achieving an impressive Mean Squared Error (MSE) of 0.0013. In comparison, ANFIS records an MSE of 0.0015, ICA-ANN 0.0090, PSO-ANN 0.0093, and ANN 0.0183.

This research examines the impact of wettability alteration on the end points of relative permeability, a crucial property of fluids and porous media that influences the displacement processes of immiscible fluids through such media. The estimation of the mobility ratio for oil recovery relies on these end points, which are influenced by connate water saturation and residual oil saturation. To investigate this relationship, carbonate rock is generally subjected to wettability alteration using surfactant agents, and core flooding is employed to determine the relative permeability before and after the alteration. The wettability of the rock is commonly assessed through contact angle measurements. Two surfactants, TritonX-100 (Tx-100) and Cedar, were tested in reducing the wettability of the porous media for oil. The contact angle measurements revealed that Tx-100 was more effective for this purpose than Cedar. Furthermore, the relative permeability tests indicated that both surfactants decreased residual oil saturation, but Tx-100 also improved system pressure. In contrast, Cedar reduced residual oil saturation but increased system pressure, possibly because of its high viscosity. The results also demonstrate that injecting Tx-100 leads to a 14% increase in ultimate oil recovery compared with water injection, while Cedar injection increased the recovery factor by 5%. This difference may be attributed to the incomplete coverage of the pore wall by Cedar or its weaker chemical structure than Tx-100. Notably, in carbonate cores, neither non-ionic surfactant enhanced oil recovery.

The use of diethylenetriaminepentaacetic acid (DTPA) chelating agent has shown promising results for enhanced oil recovery (EOR) in prior research. Several mechanisms, mainly resulting from rock-fluid interaction, have been proposed for chelating agent flooding; however, little attention has been paid to fluid-fluid interaction thus far. The assessment of these mechanisms has primarily relied on macroscopic techniques such as core flooding. This paper aims to investigate the injection of DTPA brine and its dominant mechanisms at the pore scale using a clay-coated micromodel. The micromodel tests were performed under oil-wet and water-wet states. For a more precise examination of fluid/fluid interactions, the dynamic interfacial tension (IFT) and Zeta potential were measured. It was observed that the injection of DTPA brine in water-wet state changed the saturation distribution and increased oil recovery. Based on visual inspections, this change in saturation distribution could potentially be linked to the formation of micro-dispersions and viscoelastic interfacial phenomena. Micro-dispersions facilitate flow to unswept areas, and viscoelastic interface formation reshapes the interface between oil and brine, causing disconnected oil droplets to coalesce and thus increase recovery. Under the oil-wet state, the micro-dispersion formation and wettability alteration can be the dominant mechanisms, and the amount of recovered oil was higher than that observed in the water-wet state. Furthermore, Zeta potential measurements at the interface between brine and oil showed a more negative value for DTPA brine, which is effective in wettability alteration and micro-dispersions stability. The results indicate that IFT reduction was not significant enough to be considered the dominant mechanism, although it assists in DTPA brine penetration into the crude oil and subsequent micro-dispersion formation.

In this study, a spent palm cooking oil-based mud with an excellent H₂S scavenging capability induced by the inclusion of a small quantity of potassium permanganate is formulated and tested for the first time. The mud formulation, containing the spent palm oil as the continuous phase and water as the dispersed phase, respectively, was stabilized by Span 80 and rhamnolipid biosurfactant as primary and secondary emulsifiers, respectively, while hydrophobic zinc nanoparticles (NPs) were used as weighting agent. The results showed that H₂S scavenging capacity at the breakthrough time reached 182.4 g H₂S/barrel mud, which increased to 417.9 g H₂S/barrel mud at the saturation time, demonstrating the effective H₂S scavenging performance of the formulated mud. The spent palm oil-based mud (SPOBM) also showed a good flow behavior that could be well fitted using the Herschel-Bulkley and Casson models. The effect of temperature on the apparent viscosity of the SPOBM has been investigated, and the fitting of the viscosity-temperature data provided an estimate of the activation energy as 23.53 kJ/mol. The findings reported in this article reveal the feasibility of transforming the spent/waste cooking oils into a valuable commodity for formulating greener drilling fluids with acceptable rheology and excellent H₂S scavenging performance.

This study evaluated the corrosion behavior of steel hooks embedded in GFRC, which were protected by a zinc-rich (96% Zn) galvanizing coating. The coating provided the hooks with active cathodic protection and a passive physical shield. Macrocell corrosion may form when the anode is smaller than the total steel surface. Thus, the steel hooks at the embedment juncture were additionally sealed against water ingress and air exchange using a construction sealant. The study was conducted in three phases in a salt-spray chamber. First, the electrogalvanized steel hooks embedded in GFRC were allowed to freely corrode for 7 days. In the second phase, the electrogalvanized steel hooks were painted with the zinc-rich coating and observed over 7 days. In the third phase, the steel hooks were protected by the zinc-rich coating together with a primer and construction sealant, and observed over 7 days. To evaluate the electrogalvanized hooks and the corrosion products formed, the thickness of the material was measured. Corrosion on the metal surface was inferred by studying the surface morphology of the hooks at various points of contact and after different periods of time.