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The price of oil is highly complex to predict as it is impacted by global demand and supply, geopolitical events, and market sentiment. The accuracy of such predictions, however, has far-reaching implications for supply chain performance, portfolio management, and expected stock market returns. This paper contributes to the oil price prediction literature by evaluating the predictive impact of the US President's communication on Twitter, while benchmarking various Natural Language Processing (NLP) techniques, including Term Frequency-Inverse Document Frequency (TF-IDF), Word2Vec, Doc2Vec, Global Vectors for Word Representation (GloVe), and Bidirectional Encoder Representations from Transformers (BERT). These techniques are combined with a deep neural network Long Short-Term Memory (LSTM) architecture using a five-day lag for both the oil price and the textual Twitter data. The data was collected during the term of US President Donald Trump, resulting in 1449 days of crude oil price prediction and a total of 16,457 tweets. The study is validated for Brent and West Texas Intermediate blends, using the daily price of a barrel of crude oil as the target variable. The results confirm that including the US President's tweets significantly increases the predictive power of oil price prediction models, and that an LSTM architecture with BERT as NLP technique has the best performance. [ABSTRACT FROM AUTHOR]

Constructing oil and gas wells is one of the most expensive activities in the petroleum sector, and increasing drilling speed is crucial for reducing costs, minimizing non-productive time (NPT), and improving efficiency. To address this challenge, a data-driven predictive model for mechanical drilling speed was developed using an artificial intelligence approach. Several machine learning algorithms – including decision trees, support vector regression, k-nearest neighbors, neural networks, XGBoost, and LightGBM – were selected to thoroughly investigate the nonlinear relationship between drilling data and the rate of penetration (ROP). These models were trained, evaluated, and compared, with results indicating that the LightGBM algorithm provided the most accurate ROP predictions. Building on this, the LightGBM model was further refined using cross-validation techniques and a genetic algorithm (GA) to optimize its hyperparameters, resulting in even greater accuracy. The research findings demonstrate that the GA-LightGBM algorithm achieves high precision in ROP prediction on the test set (MAE = 2.39, MSE = 10.53, R2 = 0.93). This model effectively predicts mechanical drilling speed in various well sections, thereby aiding in optimizing the drilling process, improving production efficiency, and reducing costs. HIGHLIGHTS: Utilize GA to fine-tune LightGBM hyperparameters and construct the GA_LightGBM model for ROP prediction. Utilizing artificial intelligence tools for predicting ROP. Modeling ROP prediction uncertainty using specific oilfield drilling parameters. [ABSTRACT FROM AUTHOR]

Copyright of Petroleum Geology & Oilfield Development in Daqing is the property of Editorial Department of Petroleum Geology & Oilfield Development in Daqing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The target reservoir is a typical blocky bottom water reservoir. There are several interlayers of varying scales inside it, which impact the characteristics of the oil production and water breakthrough curves of the wells in the water flooding development oilfield, resulting in strong heterogeneity within the reservoir. The Sangtamu Oilfield has an average well spacing of approximately 600 m, causing a challenge in accurately identifying the range of small-scale interlayer spreading. This, in addition, challenges the subsequent 3D geological modeling process. This paper proposes a method to accurately characterize the size of various interlayers in a reservoir by using production dynamics data from bottom water reservoirs to invert the interlayer scale. By classifying the water breakthrough curve styles derived from theoretical testing, the range of reservoir internal interlayer scales is compared and inverted based on the actual water breakthrough styles of wells in the Sangtamu oil field. In the process, many interlayer scales derived from various types of geologic data acquisition were combined to form a quantitative geologic knowledge database of interlayers that synthesizes both dynamic and static data. This method has been applied in the Tarim Basin's Sangtamu Oilfield, where the reservoir numerical simulation history was used to fit the model. This resulted in an overall fitting coincidence rate of 96% in the Sangtamu Oilfield and a single-well fitting coincidence rate of over 90% in well LN14. This method offers a new viewpoint on characterizing the interlayer's scale in the area of the non-dense well network. [ABSTRACT FROM AUTHOR]

Plug and abandonment of offshore oil wells is a costly and time-consuming process, yet it is necessary for the ever-increasing number of mature fields in the region of the Danish North Sea, as well as globally. Current practices ensuring durable solutions for the complete zonal isolation of oil wells have a large environmental impact. This paper proposes a novel resin that could be mixed on the platform and pumped into the tubing in a liquid state. The increased temperature inside the oil well initiates the cross-linking reaction of the liquid resin, creating a solid and impermeable barrier. The liquid resin is thermally stable up to 180 °C and can be handled for up to 20 h at room temperature, preventing setting before intended while decreasing environmental impact. The solid resin has a compressive strength of 54 MPa and a steel adhesion strength of 6.27 MPa, highlighting its ability to withstand extreme downhole conditions. [ABSTRACT FROM AUTHOR]

The behavior of low-carbon steels (LCSs), a high-strength steel and a nickel–chromium alloy in HCl solutions in the presence of N-containing organic substances has been studied. N-containing organic substances that comprise 1,2,4-triazole in their structure (substance I and substance II) provide comprehensive protection of various steel grades from corrosion and hydrogen absorption by the metal bulk in HCl solutions under both isobaric and isochoric conditions. All the compounds studied reduce, to varying degrees, the concentration of hydrogen adsorbed and absorbed by steel in HCl solutions. The most promising way to expand the scope of application is to use substance I in HCl solutions for protecting steels from high-temperature corrosion as a mixture with hexamethylenetetramine (HMT). In 2 M HCl (100 °C) under isochoric conditions, a mixture of compound I and HMT exhibited an excellent result: the degree of protection of LCS against corrosion was 99.5%. Substance I and its mixture with HMT protect steels not only in pure HCl solutions, but also in environments contaminated with hydrogen sulfide, which is especially important for the acid stimulation of wells in oil production. [ABSTRACT FROM AUTHOR]

Heat-resistant and low-sensitivity energetic materials are urgently needed in demanding environments, such as deep oil wells, space blasting, and hypersonic weapons. Herein, through the processes of substitution, reduction, cyclization, nitration, and ammoniation, two nitro groups and two amino groups were successfully introduced into a benzimidazole framework to prepare a new heat-resistant energetic material, 4,6-diamino-5,7-dinitro-1H-benzo[d]imidazole (DADNBI). Single crystal X-ray diffraction was executed to verify the structure of the compound. Crystal DADNBI belongs to the C2/c space group and monoclinic crystal system. The thermal stability of DADNBI was analyzed through differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA), and results showed that the decomposition temperature of DADNBI was 366 °C, which is higher than that of 2,4,6-trinitrotoluene (TNT) (Td: 295 °C), hexanitrostilbene (HNS) (Td: 318 °C), and 5,5′-bis(2,4,6-trinitrophenyl)-2,2′-bi(1,3,4-oxadiazole) (TKX-55) (Td: 335 °C) and comparable to that of 1,3,5-tritamino-2,4,6-trinitrobenzene (TATB) (Td: 360 °C). Non-isothermal thermal decomposition kinetics and Mayer bond pole calculations verified the excellent thermal stability of DADNBI from a theoretical perspective. The characteristic drop height (h50%) of DADNBI is 305 cm. All these parameters of DADNBI far exceed those of the reported 5,7-dinitro-1H-benzo[d]imidazole (DNBI). This work offers important guidelines from both theoretical and experimental perspectives for designing and synthesizing new insensitive heat-resistant energetic materials. [ABSTRACT FROM AUTHOR]

Hydrogen is a promising clean energy source with geological reserves widely distributed globally, offering an annual flow exceeding 23 trillion grams. However, natural hydrogen extraction wells face unique safety challenges compared to conventional oil and gas wells. This paper reviews well safety concerns such as tubing/casing damage, cement/sealant failure, and excessive annular pressure buildup. Key issues include hydrogen embrittlement, microbiological corrosion, H2-cement reaction, and H2-rubber degradation, which can lead to mechanical failures. The review explores potential solutions like metal coatings, rubber fillers, and cement additives to mitigate these problems. It also emphasizes the need for further research to validate these solutions under real-world conditions. Addressing these challenges is crucial for the safe and efficient extraction of natural hydrogen. In this review, authors highlight the unique safety challenges of natural hydrogen extraction wells, such as hydrogen embrittlement, microbiological corrosion, and H2-cement reaction. They identify potential solutions within the literature, emphasizing the need for more research under real-world conditions. [ABSTRACT FROM AUTHOR]

Graphene oxide (GO) has a large specific surface area, and the filler agglomeration is serious when it is mechanically mixed in open mill or internal mixer, which is unable to give full play to the layer isolation effect of GO to better improve the oil and high temperature resistance of NBR composites. Therefore, in this paper, the change rate of mass and volume, oil resistance, high temperature resistance, tear properties, Payne effect and mechanical properties of NBR composites prepared by dry mixing process, ball-milling modified GO/silica/si69-dry mixing process, NBR flocculation process, and ball-milling modified GO/silica/si69-NBR co-flocculation process were investigated. The results showed that compared with the GO/silica/NBR composites prepared by dry mixing, the change rate of tensile product and abrasion volume of the GO/silica/NBR composites prepared by ball milling-co-flocculation process after high-temperature and oil bath were reduced by 35.28% and 16.69%, respectively, and the tear strength was increased by 57.28%, which possessed excellent oil resistance and high temperature resistance, providing new technical guidance for the application of GO in oil and high temperature resistant NBR systems. [ABSTRACT FROM AUTHOR]

Low-permeability tight reservoirs have emerged as an important part of oil and gas development. Due to the rapid production decline after volume fracturing, water injection is commonly used to supply underground energy. Current rapid production prediction methods cannot account for the impact of imbibition during cyclic water injection and are unable to predict multiphase production. In this study, we established a multiphase production prediction model for cyclic water injection (i.e., huff and puff) after volume fracturing of horizontal wells. The model uses the principle of volume balance to calculate changes of the formation's physical field during the cyclic water injection process. The model employs a three-dimensional imbibition equation to calculate the oil volume of imbibition during the soak period. The model adopts a five-region nonlinear flow model to calculate total liquid production and uses relative permeability for multiphase production splitting. The results show that the model's prediction error is less than 15%. The analysis of model sensitivity factors indicates that the primary influencing factor for multiphase production is the imbibition diffusion coefficient. We have applied the multiphase production prediction method for multiple rounds of cyclic water injection. This study provides a rapid production prediction method for the development of tight oil and gas reservoirs. [ABSTRACT FROM AUTHOR]

In geoenergy science and engineering, well placement optimization is the process of determining optimal well locations and configurations to maximize economic value while considering geological, engineering, economic, and environmental constraints. This complex multi-million-dollar problem involves optimizing multiple parameters using computationally intensive reservoir simulations, often employing advanced algorithms such as optimization algorithms and machine/deep learning techniques to find near-optimal solutions efficiently while accounting for uncertainties and risks. This study proposes a hybrid workflow for determining the locations of production wells during primary oil recovery using a multi-modal convolutional neural network (M-CNN) integrated with an evolutionary optimization algorithm. The particle swarm optimization algorithm provides the M-CNN with full-physics reservoir simulation results as learning data correlating an arbitrary well location and its cumulative oil production. The M-CNN learns the correlation between near-wellbore spatial properties (e.g., porosity, permeability, pressure, and saturation) and cumulative oil production as inputs and output, respectively. The learned M-CNN predicts oil productivity at every candidate well location and selects qualified well placement scenarios. The prediction performance of the M-CNN for hydrocarbon-prolific regions is improved by adding qualified scenarios to the learning data and re-training the M-CNN. This iterative learning scheme enhances the suitability of the proxy for solving the problem of maximizing oil productivity. The validity of the proxy is tested with a benchmark model, UNISIM-I-D, in which four oil production wells are sequentially drilled. The M-CNN approach demonstrates remarkable consistency and alignment with full-physics reservoir simulation results. It achieves prediction accuracy within a 3% relative error margin, while significantly reducing computational costs to just 11.18% of those associated with full-physics reservoir simulations. Moreover, the M-CNN-optimized well placement strategy yields a substantial 47.40% improvement in field cumulative oil production compared to the original configuration. These findings underscore the M-CNN's effectiveness in sequential well placement optimization, striking an optimal balance between predictive accuracy and computational efficiency. The method's ability to dramatically reduce processing time while maintaining high accuracy makes it a valuable tool for enhancing oil field productivity and streamlining reservoir management decisions. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Shale gas, as an important component of unconventional energy, holds enormous potential value in the energy sector. However, due to the complex geological characteristics and fluid flow mechanisms of shale gas reservoirs, its exploitation faces numerous challenges. This study focuses on the optimization of intermittent production methods for shale gas wells in the Changning block. In this study, a dynamic coordination model of formation recharge and wellhead output was established using real-time pressure monitoring and historical production records as key inputs. Based on this, the dimensionless production efficiency index was optimized by finely regulating the switching timing of the wellhead, thus significantly enhancing the cumulative oil production of the well. The conclusions indicate that the optimization methods proposed in this study can effectively guide the production operations of shale gas wells in the Changning block, thereby enhancing production yield and stability. This research contributes practical value to the field by offering theoretical support and practical guidance for shale gas exploitation, addressing technical challenges in the process. [ABSTRACT FROM AUTHOR]

The heterogeneity and complexity of low-permeability oil and gas reservoirs pose significant technical challenges for their efficient development, including reservoir plugging, high flow resistance, and optimal well type design issues. These challenges result in high development costs and extended production cycles due to insufficient productivity of oil and gas wells. Therefore, accurately assessing the applicability of horizontal wells and their design parameters in the development of low-permeability reservoirs through oil and gas well productivity prediction has become a pressing key issue that needs to be addressed. In this study, based on the principle of well type optimization in the development of low-permeability oil and gas reservoirs, the adaptability of horizontal wells is evaluated using steady-state productivity methods, and their stimulation effects are predicted and analyzed. A systematic comparison of the steady-state productivity of horizontal wells and vertical wells is conducted, productivity predictions for different types of gas reservoirs are conducted, the design parameters of horizontal wells (highly deviated wells) are optimized, and a well type adaptability evaluation system suitable for different reservoir characteristics is formed. The research findings indicate that vertical wells are preferable for gas wells when the steady-state productivity ratio (HRV) is less than 1. However, when the HRV is greater than or equal to 1, the benefits of horizontal and highly deviated wells become notably superior to those of vertical wells. Taking into account the output value, cost, and net income of the gas well, the optimal outcome is achieved with a horizontal section length of 800 m. Ultra-low-permeability thick reservoirs are best suited to vertical wells, low-permeability thick reservoirs to highly deviated wells, and low-permeability thin reservoirs to horizontal wells. The assessment of steady-state productivity offers a theoretical foundation for optimizing development plans in low-permeability oil and gas reservoirs. [ABSTRACT FROM AUTHOR]

The light oil wells within the Neogene Shawan Formation have been extensively drilled in the Chepaizi Uplift, reflecting an increase that provides new targets for unconventional resources in the Junggar Basin of northwestern China. However, the original sources of light oil remain controversial, as several source rocks could potentially generate the oil. For this study, we collected light oils and sandstone cores for biomarker detection using gas chromatography–mass spectrometry (GC-MS). Additionally, fluid inclusions were observed and described, and the homogenization temperatures of saltwater inclusions were measured to confirm the oil charging history in conjunction with well burial and thermal history analysis. Based on these geochemical characteristics and carbon isotopic analysis, the results indicate that light oil in the Chepaizi Uplift zone primarily originates from Jurassic hydrocarbon source rocks in the Sikeshu depression, with some contribution from Cretaceous hydrocarbon source rocks. Jurassic hydrocarbon source rocks reached a peak of hydrocarbon generation in the middle to late Neogene. The resulting crude oil predominantly migrated along unconformities or faults to accumulate at the bottom of the Cretaceous or Tertiary Shawan Formation, forming anticlinal or lithologic oil reservoirs. Some oil reservoirs contain mixtures of Cretaceous immature crude oil. During the Neogene light oil accumulation process, the burial rate of reservoirs was high, and the efficiency of charging and hydrocarbon supply was relatively high as well. Minimal loss occurred during the migration of light oil, which significantly contributed to its rapid accumulation. [ABSTRACT FROM AUTHOR]

To reduce environmental risks and impacts from orphaned wells (abandoned oil and gas wells), it is essential to first locate and then plug these wells. Manual reading and digitizing of information from historical documents is not feasible, given the large number of wells. Here, we propose a new computational approach for rapidly and cost-effectively characterizing these wells. Specifically, we leverage the advanced capabilities of large language models (LLMs) to extract vital information including well location and depth from historical records of orphaned wells. In this paper, we present an information extraction workflow based on open-source Llama 2 models and test it on a dataset of 160 well documents. The developed workflow achieves an overall accuracy of 100%, accounting for both text conversion and LLM analysis when applied to clean, PDF-based reports. However, it struggles with unstructured image-based well records, where accuracy drops to 70%. The workflow provides significant benefits over manual human digitization, because it reduces labor and increases automation. Additionally, more detailed prompting leads to improved information extraction, and LLMs with more parameters typically perform better. Given that a vast amount of geoscientific information is locked up in old documents, this work demonstrates that recent breakthroughs in LLMs allow us to access and utilize this information more effectively. [ABSTRACT FROM AUTHOR]

The application of classic foaming agent faces several issues, including excessive use of defoaming agent, inadequate defoaming, pipeline blockage due to silicone oil precipitation, and high development cost of the foaming agent. To address the aforementioned issues, a novel intelligent foaming agent was created. This resulted in the development of a new intelligent foaming and discharging agent system. The study focused on analyzing key performance indicators of the foaming agent system, including temperature resistance, salt resistance, oil resistance, phase transition temperature point, foaming ability, foam half-life, liquid carrying capacity, and self-defoaming ability. The experimental findings indicate that TS-1 and ESAB exhibit favorable foaming performance and stability under the conditions of 90 °C temperature, 20 × 104 mg/L salinity, and 40% condensate oil content after a 1:1 mixture. Additionally, they are capable of undergoing phase transition within the temperature range of 12 to 15.2 °C. The Waring blender stirring method resulted in the foaming agent solution, which had a concentration of 3 g/L, reaching a volume of 487 mL. The foam's half-life was 20 min, and the liquid carrying rate was 91.7%. After a duration of 20 min, the rate of self-defoaming was 81.6%. The addition of the self-developed synergist facilitated the defoaming process, which was successfully accomplished within a time frame of 10 min. Moreover, the self-defoaming rate achieved a remarkable 100%. The foam drainage agent system may autonomously react to variations in ambient temperature and achieve phase transition behavior through temperature stimulation. This is accomplished by utilizing the natural temperature difference between the bottom hole and the wellhead during foam drainage gas recovery operations. This innovation presents a novel concept for the foam drainage agent used in recovering drainage gas. It simplifies the operation of gas recovery in oil and gas wells, provides solutions for further smartening up oil and gas fields. It holds immense theoretical and practical importance. [ABSTRACT FROM AUTHOR]

Copyright of Oilfield Chemistry is the property of Sichuan University, Oilfield Chemistry Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Oilfield Chemistry is the property of Sichuan University, Oilfield Chemistry Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Mixed organic and mineral deposits clogging perforations in the near wellbore area is a significant problem in the oil and gas industry. Matrix acidizing is widely applied to relieve this damage in the Haoud Berkaoui region using three different acid systems proposed by company services to restore the initial properties. This work aims to understand and interpret the history to investigate the efficient acid system adapted to the reservoir of two candidate wells. Different data are collected and analyzed. However, laboratory tests were conducted to verify the performance of each acid. Matching laboratory results, Acid Response Curves (ARC) interpretation, and microscopic photos with field data analysis show that only one mud acid system (6% HCl-1.5% HF) among the three is adequately used in the field to minimize the skin factor. It also increases the relative permeability in the wellbore region (7 for well N1 and 3.7 for well N2) and a flow rate gain (0.25 m3/h for the well N2). This study allows the best acid selection and suitable additives for maximum oil recovery of wells, solving problems associated with the production of wells and decreasing the cost of the operation in the future. [ABSTRACT FROM AUTHOR]

This work presents a study of radioactivity in soil samples from oil well sites of Baba Gurgur dome of Kirkuk oil field using a high-purity germanium (HPGe) detector and spectroscopy system. The activity concentration (Bq kg−1) of 226Ra, 232Th, 40K and 137Cs varied between 26.4–98, 10.8–55.2, 239–729, and 2.40–7.20, respectively. The results have been compared with the average worldwide values. The hazard indices have also been calculated. The radium equivalent activity (Raeq) was 131.04 Bq kg−1, the absorbed gamma dose rate (D) was 62.04 nGy h−1, the annual effective dose rate (AEDE) outdoor and indoor were 0.08 mSv y−1 and 0.30 mSv y−1, respectively, the external hazard (Hex) was 0.35, the internal hazard (Hin) was 0.51, the Gamma radiation representative level Index (Iγ) was 0.96, and excess lifetime cancer risk was 0.30 × 10−3. Our results of some sites were found to be higher than the allowable worldwide values. Statistical data including: Mean, Median, Std. Deviation, kurtosis, skewness, box plot, frequency distribution, Quantile-quantile (Q-Q) plots, Pearson's correlation coefficient analysis, principal component analysis (PCA), and cluster analysis were calculated and carried out to explain our results. [ABSTRACT FROM AUTHOR]

One of the main directions of environmental protection and pollution prevention is the improvement of technological processes. The drilling of deep oil and gas wells is a very complex technological process, the violation of which can lead to various emergencies that pollute the environment. Therefore, well rehabilitation is becoming an increasingly important aspect of ensuring the sustainability and efficiency of the production process. This paper analyses the performance of equipment used in oil well rehabilitation. For this purpose, a classification of accidents in production and drilling wells is presented and a qualitative evaluation of the main oil well rehabilitation methods and the equipment used in this process (milling, capture, flushing tools and raisers) is carried out in order to determine their advantages and disadvantages. [ABSTRACT FROM AUTHOR]

The reliability of the system for collecting products from oil, gas and gas condensate fields directly depends on the condition of the well flow lines. Many years of experience in the exploitation of offshore fields in the Azerbaijani sector of the Caspian Sea shows that various kinds of complications arise in the flow lines of wells associated with salt deposits, asphalt base, tarry oil and paraffin deposits, fluid accumulation, pressure pulsations, etc. The listed complications lead to the formation of blockages in the flow lines, an increase in the mechanical load on the equipment and, ultimately, to a decrease in the productivity of lines and wells, up to the point of stopping their operation. Work to restore these lines takes quite a long time and requires significant costs. Reducing technological risks in the operation of well flow lines is closely related to diagnosing their condition. But planned diagnostic work involving forces and equipment often in practice reveals complications at a late stage of their development and engineering decisions are made to shut down wells and restore lines. In connection with the above, the issues of diagnosing complications at an early stage of their formation acquire current and practical importance. Timely cleaning of flow lines and regulation of operating conditions of production wells, such as the selection of an adequate wellhead pressure regime and other technological solutions, can prevent a negative scenario for the development of complications. In turn, for early diagnosis of complications it is necessary to find a diagnostic criterion. determining the beginning of their formation. Due to the fact that the main flow parameters at the metering nodes are continuously recorded in real time, it would be advisable to develop an appropriate diagnostic criterion based on the results of studying the dynamics of time series for the main indicators of flow lines. For this purpose, in this work, flow lines of wells in the Bulla-Deniz field (Azerbaijan) were studied. The main indicators for the studied lines were length and diameter, pressure at the beginning and end of the line, gas factor and line productivity. Based on these indicators, data processing was carried out and corresponding statistical estimates were obtained. The results of the analysis revealed a stable correlation between gas factor indicators and flow line performance. It was established that a sharp decrease in line productivity with an increase in the gas factor manifested itself in wells in which restoration work was carried out due to complications that arose. This makes it possible to use this behavior of the curves of the dependence of flow line productivity on the gas factor as a criterion for diagnosing complications at an early stage of their formation. The results of this research can be recommended for use in the operation of a system for collecting and transporting oil and gas field products. [ABSTRACT FROM AUTHOR]

Objectives To clarify the corrosion failure causes of N80 tubing in a production well of heavy oil fire flooding in Xinjiang oilfield, and to identify the main control factors and corrosion mechanism. Methods The service history and macroscopic corrosion morphology of the fire-drive production well tubing were analyzed. The chemical composition, metallographic structure and mechanical properties of the failed tubing were analyzed. The corrosion products were analyzed by SEM, EDS and XRD. Results The chemical composition and mechanical properties of the failed tubing were in line with the relevant standards, and the metallographic structure was normal. The oil pipe was mainly corroded locally, and the outer diameter of the oil pipe was reduced by 1.89 mm after only 1 year of service. The corrosion product layer has a thickness of 96 μm and is mainly composed of Fe2O3, FeCO3 and FeOOH. Conclusions CO2 corrosion and O2 corrosion have occurred in the failed tubing at high temperatures, and the synergistic effect of O2 and Cl-was the main cause leading to the development of local corrosion in the tubing. It is recommended that Al alloy sacrificial anode short sections be used for protection, and the research results provide a reference for the corrosion and protection of tubing string in fire-drive environment. [ABSTRACT FROM AUTHOR]

During CO2 transportation and storage, metal equipment such as oilfield pipelines suffers from severe CO2 corrosion, especially in harsh downhole injection equipment. In this study, we investigated the corrosion behavior of oil well tubing in a high-temperature, high-pressure (HTHP) CO2-containing environment. The evolution of the corrosion scale was also examined under different flow regimes. The results reveal a lower corrosion rate at 150 °C compared to 80 °C under different flow regimes, with localized corrosion intensifying as temperature and rotational speeds (vrs) increase. The temperature also induces the corrosion scale conversion of aragonite-type CaCO3 (80 °C) to calcite-type CaCO3 (150 °C). Specifically, the variation of the corrosion rate and the corrosion scale evolution can be attributed to the vortices within the reactor. The intact vortex cells enhance mass transfer while also promoting nucleation and growth of CaCO3. However, when vrs exceeds the critical Reynolds number, the vortex cells are disrupted, resulting in viscous dissipation and a reduced corrosion rate. [ABSTRACT FROM AUTHOR]

The application of perforating completion technology in oil and gas field development has gained widespread popularity. Enhancing the efficiency of oil and gas wells relies on increasing the penetration depth, which is influenced by the design of the perforation charge and the strength characteristics of the rock material. However, as a crucial objective function for optimizing perforating charge structures, jet velocity lacks a rapid and accurate calculating method. This article addresses this issue by proposing an improved collapse velocity model using the DP46RDX42-Y perforating charge as a case study. It presents a novel approach for calculating jet velocity based on the unsteady Pugh-Eichelberger-Rostoker (PER) theory. To validate the effectiveness of the proposed method and analyze the impact of different characteristic parameters on jet tip velocity, a series of numerical simulations were conducted using LS-DYNA software combined with Arbitrary Lagrange-Euler (ALE) techniques. Results indicate excellent agreement between the proposed method and the numerical results, demonstrating its superiority over the traditional Gurney formula with an impressive 34.15% increase in accuracy. Notably, this method is particularly suitable for perforating charges with low detonation velocity. Increasing the liner density and decreasing the liner thickness and cone angle is recommended to achieve higher jet tip velocity. Furthermore, the proposed method has the potential for broader application in other perforating charges with varying liner shapes. This study provides a comprehensive and efficient solution for calculating jet velocity, which facilitates optimizing perforating charge structures and calculating penetration depth [ABSTRACT FROM AUTHOR]

The shale revolution has turned the United States from an oil importer into an oil exporter. The success of shale oil production in the U.S. has inspired many countries, including China, to begin the exploitation and development of shale oil resources. In this study, the production curves of over 30,000 shale oil wells in the Bakken, Eagle Ford (EF) and Permian are systematically analyzed to provide reference and guidance for future shale oil development. To find out the most suitable decline curve models for shale oil wells, fifteen models and a new fitting method are tested on wells with production history over 6 years. Interestingly, all basins show similar results despite of their varieties in geological conditions: stretched exponential production decline (SEPD) + Arps model provides most accurate prediction of estimated ultimate recovery (EUR) for wells with over 2 years' production, while the Arps model can be used before the two years’ switch point. With the EUR calculated by decline curve analysis, we further construct simple regression models for different basins to predict the EUR quickly and early. This work helps us better understand the production of shale oil wells, as well as provide important suggestions for the choices of models for shale oil production prediction. [ABSTRACT FROM AUTHOR]

Temporary plugging and diverting fracturing (TPDF), involving inner-fracture temporary plugging (IFTP) and inner-stage temporary plugging (ISTP), has been proposed as a widely applied technique in China, for promoting the uniform initiation and propagation of multi-clustered hydraulic fractures (HFs) in a horizontal well of the shale oil/gas reservoirs. However, how the key plugging parameters controlling the multi-fracture growth and the pumping pressure response during TPDF in shale with dense bedding planes (BPs) and natural fractures (NFs) is still unclear, which limits the optimization of TPDF scheme. In this paper, a series of TPDF simulation experiments within a stage of multi-cluster in a horizontal well were carried out on outcrops of Longmaxi Formation shale using a large-scale true tri-axial fracturing simulation system, combined with the acoustic emission (AE) monitor and computed tomography (CT) scanning techniques. Each experiment was divided into three stages, including the conventional fracturing (CF), IFTP and ISTP. Multi-fracture initiation and propagation behavior, and the dominant controlling parameters were examined, containing the particle sizes, concentration of temporary plugging agent (TPA), and cluster number. The results showed that the number of transverse HFs (THFs) and the overall complexity of fracture morphology increase with the increase in TPA concentration and perforation cluster number. Obviously, the required concentration of TPA is positively correlated with the cluster number. Higher peak values and continuous fluctuations of pumping pressure during TPDF may indicate the creation of diversion fractures. The creation of standard THFs during CF is favorable to the creation of diversion fractures during TPDF. Moreover, the activation of BPs nearby the wellbore during CF is unfavorable to the subsequent pressure buildup during TPDF, resulting in poor plugging and diverting effect. Notably, under the strike-slip fault stress regime, the diversion of THFs is not likely during IFTP, which is similar as the results of ISTP to initiate mainly the un-initiated or under-propagated perforation clusters. Three typical pressure curve types during TPDF can be summarized to briefly identify the hydraulic fracture diversion effects, including good (multiple branches or/and THFs can be newly created), fair (HF initiation along the slightly opened BPs and then activating the NFs), and bad (HF initiation along the largely opened BPs and then connecting with the NFs). [ABSTRACT FROM AUTHOR]

Shale oil reservoirs are characterized by extremely low porosity and permeability, necessitating the utilization of multi-fractured horizontal wells (MFHWs) for their development. Additionally, the complex phase behavior and desorption effect of two-phase fluids make the fluid flow characteristics of shale oil reservoirs exceptionally intricate. However, there are no productivity models for MFHWs in shale oil reservoirs that incorporate the complex hydraulically fractured networks, the oil–gas desorption effect, and the phase change of oil and gas. In this study, we propose a novel productivity model for MFHWs in shale oil reservoirs that incorporates these complex factors. The conformal transformation, fractal theory, and pressure superposition principle are used to establish and solve the proposed model. The proposed model has been validated by comparing its predicted results with the field data and numerical simulation results. A detailed analysis is conducted on the factors that influence the productivity of shale oil wells. It is found that the phase behavior results in a significant 33% reduction in well productivity, while the fluid desorption leads to a significant 75% increase in well productivity. In summary, the proposed model has demonstrated promising practical applicability in predicting the productivity of MFHWs in shale oil reservoirs. [ABSTRACT FROM AUTHOR]

Currently, most oil and gas wells are drilled by continuously transmitting downhole measured information (directional and geological information) in real-time to the surface to monitor and steer the well along a pre-defined path. The intelligent drill pipe method can transmit data over longer distances and at a higher rate than other methods, such as mud pulse telemetry, acoustic telemetry, and electromagnetic telemetry. Nevertheless, it is expensive and requires boosters along the drill string. In the available literature, academic research rarely addresses the data transmission process in boreholes using intelligent drill pipes. Furthermore, there is a need for an effective and validated model to study various controllable parameters to enhance the efficiency of the intelligent drill pipe telemetry without the need to develop several physical lab or field prototypes. This paper presents the development of a model based on MATLAB Simulink to simulate the process of data transmission in boreholes utilizing intelligent drill pipes. Laboratory experimental prototype measurements have been used to test the model's effectiveness. A good correlation is found between the measured lab data and the model's predictions for the signals transmitted contactless through intelligent drill pipes with a correlation coefficient (R2) above 0.9. This model can enhance data transmission efficiency via intelligent drill pipes, study different concepts, and eliminate the need to develop several unnecessarily expensive and time-consuming physical lab prototypes. [ABSTRACT FROM AUTHOR]

Honing the quality of cement plays a pivotal role in the petroleum industry throughout drilling operations, undoubtedly. In cement plants, producing high-quality clinker is essential for improving oil well maintenance, reducing expenditures, enhancing safety, and more, demonstrating its importance in the long term. Despite tight control of clinker operational conditions, cement kiln operators can still produce dusty clinker. This work uniquely establishes the previously unexplored relationship between dusty clinker and the qualitative characteristics of class G oil well cement slurries, offering a practical solution to prevent the production of poor-quality cement in factories. All API tests were done according to API Specification 10 A. Furthermore, a thorough microscopic analysis was conducted on both standard and dusty clinkers to establish a connection between the properties of class G oil well cement slurries and the changes in crystal size observed in the minerals of dusty clinker. According to the studies conducted in this regard, changing the size of the cement particles has a considerable influence on the cement performance. Here, results revealed that increasing the alite (C3S) crystal size to 60–70 microns in dusty clinker led to a roughly 20-minute increase in the average thickening time. In addition, the compressive strength cured at 38 and 60 °C for 8 h, decreased by 1.47 and 2.89 MPa, respectively. More importantly, it was found that the average maximum consistency in dusty clinker oil well cement increased by almost 6.4 Bc over a 15 to 30-minute period. [ABSTRACT FROM AUTHOR]

Oil well productivity capacity is an important parameter in oilfield development, which is of great significance for efficient development. Traditional oil well productivity capacity prediction methods have a series of problems, such as limited application scope, large prediction errors, difficulty in characterizing changes under the influence of multiple factors. Aiming at these problems, a well productivity prediction method based on machine learning algorithm was proposed. Taking Bohai X oilfield as the research object, 12 factors affecting oil well productivity capacity were selected from three aspects: geology, engineering, and production. The degree of each factor influence on oil well productivity capacity was analyzed by using the mean decrease impurity (MDI) method, the feature parameters were sequentially excluded, and redundant features that do not affect the prediction accuracy of the model were removed. And then support vector machine (SVM) optimized by improved whale optimization algorithm (IWOA) was used to establish prediction model for oil well productivity capacity. The results show that the main control factors of oil well productivity capacity are: permeability, porosity, effective thickness, pressure draw-down, perforation thickness, fracturing sand addition amount, resistivity, oil saturation, sand addition strength and shale content. The model based on SVM optimized by the improved whale algorithm have an average error of 9.3%, while the model based on SVM optimized by grid search and whale algorithm have bigger errors, which are 21.7% and 15.7% respectively. Residual sum of squares (R2) values for SVM optimized by grid search optimization, whale algorithm and improved whale algorithm are 0.372, 0.939 and 0.941 respectively. The model based on SVM optimized by the improved whale algorithm has higher accuracy in predicting oil well productivity capacity. Compared with existing literature, the MDI method was used to optimize the factors affecting oil well productivity, and IWOA was used to improve the accuracy of oil well productivity capacity prediction. The research results can provide reference for the well productivity capacity prediction. [ABSTRACT FROM AUTHOR]

The subject sandstone reservoir is highly heterogeneous due to amalgamated tidal-influenced distributary channels. In addition, it has an unfavorable harsh environment for chemical enhanced-oil-recovery (EOR), such as high salinity (280,000 ppm), temperature (200 °F), and divalent ions (19,000 ppm) as Ca + + and Mg + +. After a robust lab alkaline-surfactant-polymer (ASP) formulation design and successful field trial of the single-well-chemical-tracer-test (SWCTT), an inverted 5-spot injection pattern ASP EOR pilot was designed and field execution started. This pilot is having a challenge controlling the elevated drizzle inorganic carbonate scale at the producers. This paper elaborates on novel practical strategies to control elevated scaling risks and the re-optimization of ASP formulation adjusted to temperature variations due to the extended softened-water pre-flush phase. An extended soften-water pre-flush phase (over-flush) was considered as 7-pore-volume injection (PVI) to avoid the anticipated drizzle scaling risk. However, this over-flush strategy results in longer pilot duration, significantly higher soften water injection costs, reservoir cooling in the pilot region, and persistently high carbonate scaling in the producers. In view of this, various novel pre-flush operating strategies are investigated using high-resolution numerical simulations. The optimized pre-flush strategy was chosen based on minimal divalent ion production to control scale. Thermal simulation shows that this optimized pre-flush strategy resulted in significant cooling of the reservoir in the pilot area prior to ASP injection. Due to this cooling, the lab testing raises serious concerns about the effectiveness of the originally designed ASP formulation at reservoir temperature. As a result, the ASP formulation underwent meticulous re-tuning to ensure its robustness against temperature fluctuations and the harsh unfavorable reservoir environment. The re-optimized robust ASP formulation shows effectiveness in large temperature variations and the harsh environment of the pilot. The results of the optimized novel pre-flush strategy demonstrate that elevated drizzle scale can be controlled under harsh environments using viscous polymer slug injection (∼0.6 to 0.75 PVI) with soften water injection (~ 2 PVI). This novel strategy not only controls the scaling risk but also provides better oil desaturation, a better oil chemical ratio, less cooling effect, and a shorter pilot duration (thereby better economics) as compared to the originally designed pre-flush strategy (7PVI). [ABSTRACT FROM AUTHOR]

In view of the deep burial depth, high formation pressure, and presence of top and bottom water in offshore extra-heavy-oil reservoirs, this paper conducts a study on the production performance and flow field variation law of steam huff-and-puff to steam flooding conversion in thick heavy-oil reservoirs based on physical simulation, and analyzes the development effect of the conversion from steam huff-and-puff to steam flooding. On this basis, by comprehensively considering the advantages of gravity-assisted steam flooding and a three-dimensional HHSD well pattern obtained from physical simulation experiments, this paper proposes a well pattern development mode of steam huff-and-puff to composite displacement and drainage, and analyzes the development effect of this well pattern mode using the reservoir numerical simulation method. The research results show that, compared with the planar well pattern of steam huff-and-puff to steam flooding conversion, the adoption of the three-dimensional well pattern can significantly improve the degree of reservoir production and the expansion dynamics of the steam chamber, and mitigate adverse effects such as the increase in water cut caused by top and bottom water on thermal recovery. The composite development of steam huff-and-puff to composite displacement and drainage can be divided into three stages: thermal communication, gravity drainage-assisted steam flooding, and thermal breakthrough erosion and oil washing. The steam chamber presents a development mode of "single-point development–rapid longitudinal expansion–rapid transverse expansion upon reaching the top–polymerization into a sheet", and simultaneously possesses the oil displacement mechanisms of both steam displacement and gravity drainage. The proposed composite mode of steam huff-and-puff to composite displacement and drainage has guided the implementation of adjustment wells in the Bohai L Oilfield, and the recovery factor has been increased by about 20% compared with the steam huff-and-puff development of the basic well pattern. This study has reference and guiding significance for the efficient thermal recovery development of this oilfield. [ABSTRACT FROM AUTHOR]

IoT sensors in oilfields gather real-time data sequences from oil wells. Accurate trend predictions of these data are crucial for production optimization and failure forecasting. However, oil well time series data exhibit strong nonlinearity, requiring not only precise trend prediction but also the estimation of uncertainty intervals. This paper first proposed a data denoising method based on Variational Mode Decomposition (VMD) and Long Short-Term Memory (LSTM) to reduce the noise present in oil well time series data. Subsequently, an SDMI loss function was introduced, combining the respective advantages of Soft Dynamic Time Warping and Mean Squared Error (MSE). The loss function additionally accepts the upper and lower bounds of the uncertainty prediction interval as input and is optimized with the prediction sequence. By predicting the data of the next 48 data points, the prediction results using the SDMI loss function and the existing three common loss functions are compared on multiple data sets. The prediction results before and after data denoising are compared and the results of predicting the uncertainty interval are shown. The experimental results demonstrate that the average coverage rate of the predicted uncertainty intervals across data from seven wells is 81.4%, and the prediction results accurately reflect the trends in real data. [ABSTRACT FROM AUTHOR]

Steam-Assisted Gravity Drainage (SAGD) method is recognized as one of the most effective methods for the recovery of heavy oil and natural bitumen. This technology has received several modifications throughout its history designed to improve it. One of the promising modifications is the Single Well-SAGD (SW-SAGD), which allows significantly reducing the CAPEX for drilling a well. However, this method has several disadvantages such as steam breakthrough into the production part and the uneven development of the steam chamber along the well. This article presents the concept of a Single-Well Cyclic SAGD (SWCSAGD), which allows preventing the breakthrough of the injected steam into the production section and the uniform development of the steam chamber along the well. The comparison analysis of the developed modification of SWC-SAGD was carried out using the classical method of 3D hydrodynamic modeling of both options using the example of one of the fields of the Republic of Kazakhstan. The results of the work show the efficiency of the proposed technology in terms of field oil total production. [ABSTRACT FROM AUTHOR]

Poor wellbore cleaning is a significant challenge in oil drilling, primarily due to the accumulation of cuttings at the bottom of the well, particularly in deviated and horizontal wells. This study addresses this issue by employing Computational Fluid Dynamics (CFD) with the commercial software ANSYS FLUENT (2023-R1) to simulate a solid–liquid multiphase flow in an annulus. The primary objective is to analyze the cuttings concentration, pressure loss, and solid velocity profiles across various drilling parameters, including drill pipe rotation, the flow rate, rate of penetration, inclination angle, and fluid rheology. Our results underscore the critical role of these parameters in enhancing cuttings transport efficiency. Specifically, the drill pipe rotation, flow rate, and rate of penetration emerge as the most influential factors affecting the wellbore cleaning performance. With a validated model exhibiting an average error of 4.24%, this study provides insights into optimizing drilling operations to improve wellbore cleaning and increase hydrocarbon recovery. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Petroleum Research & Studies is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Petroleum Research & Studies is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Steel Pipe is the property of Steel Pipe Magazine and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Steel Pipe is the property of Steel Pipe Magazine and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the process of developing mature deposits, a number of geological and technological complications arise. In order to increase the smooth operation of downhole pumping equipment in oil and gas wells, companies use various methods and techniques. This article presents a novel methodology for predicting downhole pumping equipment failures. A detailed analysis was conducted on historical data regarding downhole pumping equipment failures, which were then incorporated into algorithms to calculate the operation of downhole equipment. As a result, it was discovered that in order to predict failures of downhole equipment, it is crucial to consider the historical data of the field and perform an assessment of the well's potential. In the process of building a failure prediction model, the authors encountered the quality and completeness of historical data from the pilot field. They concluded that the data classes needed to be more balanced. The authors applied machine learning approaches to an imbalanced dataset. The significance of our approach lies in its ability to forecast equipment failures, thereby ensuring the smooth operation of wells operated by sucker rod pumps. [ABSTRACT FROM AUTHOR]

Copyright of Iraqi Journal of Chemical & Petroleum Engineering is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)