Search Results (34)

The optimization of pump setting depth in low-permeability oil wells remains a persistent challenge, as conventional inflow performance relationship (IPR) curves fail to capture the coupled effects of downhole pump intake depth and reservoir productivity. To address this limitation, this study proposes a novel Low-Permeability Intake Inflow Performance Relationship (LIIPR) framework. The method establishes a theoretical link between pump depth and production by integrating low-permeability reservoir inflow models with multiphase wellbore flow calculations. On this basis, a series of derivative concepts and analytical tools are introduced, including (i) a three-zone classification of inflow curves to distinguish effective, inefficient, and abnormal production regimes; (ii) a multi-pump-depth analysis to determine the feasible range and optimal boundaries of pump setting depth; and (iii) a three-dimensional deep-pumping limit map that couples inflow and outflow dynamics through nodal analysis, providing a comprehensive criterion for system optimization. The proposed LIIPR methodology enables accurate identification of optimal pump depth and intake pressure conditions, overcoming the ambiguity of traditional IPR-based approaches. Unlike previous IPR- or EIPR-based methods, LIIPR introduces for the first time a unified inflow–outflow coupling framework that quantitatively links pump intake depth with well productivity. This integration represents a novel theoretical and computational advance for deep-pumping optimization in low-permeability reservoirs. Applications for field cases in Shengli Oilfield confirm the theoretical findings and demonstrate the practical potential of the method for guiding efficient deep pumping operations in low-permeability reservoirs. Full article

Gypsum and salt rocks have been proven to act as seals for abundant oil and gas reserves on a global scale, with significant potential for hydrocarbon preservation and evolution. Notably, the sedimentary dynamics of non-evaporitic gypsum in terrestrial half-graben basins remain underexplored, particularly regarding its genetic link to hydrocarbon accumulation in interbedded mudstones. This study is based on the Zhanhua Sag, in which thick-layered gypsum rocks with dark mudstone are deposited. The gypsum crystals show the intermittent deposition characteristics. The cumulative thickness of the gypsum-containing section reaches a maximum of over 110 m. The spatial distribution of gypsum thickness correlates strongly with the location of deep-seated faults. The strontium and sulfur isotopes of gypsum indicate deep hydrothermal fluids as mineral sources, and negative oxygen isotope excursions also suggest that gypsum layers precipitated in situ from hot brine. Total organic carbon and Rock-Eval data indicate that the deep-lake gypsum rock system has excellent hydrocarbon potential, especially in the mudstone interlayers. This study developed a depositional model of deep-lake gypsum rocks with thermal brine genesis in half-graben basins. The gypsum-bearing system is rich in mudstone interlayers. These gypsum–mudstone interbeds represent promising targets for shale oil exploration after the initial breakthrough during the extraction process. These insights provide a theoretical framework for understanding gypsum-related petroleum systems in half-graben basins across the globe, offering guidance for hydrocarbon exploration in analogous sedimentary environments. Full article

This paper presents an optimized economic evaluation model for intensive drilling in unconventional oil and gas development, based on the “well factory” concept. The model integrates cost components—including pre-drilling, drilling operations, drilling materials, and drilling fluid treatment—with synergy factors that capture the benefits of centralized operations. These synergy factors reflect cost savings arising from reduced land acquisition, optimized rig sharing, and enhanced reuse of drilling fluids. Furthermore, a decision timeliness component is incorporated into the objective function, ensuring that the model can dynamically balance cost minimization with rapid decision making—a critical requirement in fast-paced energy projects. The model is rigorously developed by building on conventional cost equations and is validated using field data from the Shengli Oilfield Yan 227 block. The results demonstrate significant reductions in pre-drilling and drilling operation costs under the “well factory” mode compared to conventional single-well approaches. Additionally, an optimal platform configuration is identified, highlighting the economic benefits of integrating centralized infrastructure and shared resources in large-scale drilling operations. This comprehensive framework provides a robust decision-support tool for enhancing economic efficiency and operational effectiveness in the development of unconventional resources. Full article

The low-permeability gravity-flow sandstone reservoirs in the Dongying Depression, China, contain substantial oil reserves, yet their development is constrained by complex pore structures. In this study, optical and scanning electron microscopy (SEM) observations were integrated with nuclear magnetic resonance (NMR) measurements to investigate the pore system, pore size distribution, and connectivity of Es3z sandstone. By applying a Gaussian multi-peak fitting algorithm to the NMR T₂ spectra, parameters that directly capture the physical attributes of the rocks were extracted. Based on the correlation between these parameters and permeability, three distinct pore structure types (A, B, and C) were identified. The results demonstrate the effectiveness of using these NMR T₂ spectral parameters for quantitative pore structure characterization and classification, providing a robust framework for evaluating and predicting the quality of low-permeability reservoirs. Full article

Methane (CH₄) is the second-largest greenhouse gas contributing to global climate warming. As of 2022, methane emissions from the oil and gas industry amounted to 3.586 million tons, representing 13.24% of total methane emissions and ranking second among all methane emission sources. To effectively control methane emissions in oilfield regions, this study proposes a multi-source remote sensing data fusion method based on the concept of data fusion, targeting high-emission areas such as oil and gas fields. The aim is to construct an XCH₄ remote sensing dataset that meets the requirements for high resolution, wide coverage, and high accuracy. Initially, XCH₄ data products from the GOSAT satellite and the TROPOMI sensor are matched both spatially and temporally. Subsequently, variables such as longitude, latitude, aerosol optical depth, surface albedo, digital elevation model (DEM), and month are incorporated. Using a local random forest (LRF) model for fusion, the resulting product combines the high accuracy of GOSAT data with the wide coverage of TROPOMI data. On this basis, ΔXCH₄ is derived using GF-5. Combined with the GFEI prior emission inventory, the high-precision fusion dataset output by the LRF model is redistributed grid by grid in oilfield areas, producing a 1 km resolution XCH₄ grid product, thereby constructing a high-precision, high-resolution dataset for oilfield regions. Finally, the challenges that emerged from the study were discussed and summarized, and it was envisioned that, in the future, with the advancement of satellite technology and algorithms, it would be possible to obtain more accurate and high-resolution datasets of methane concentration and apply such datasets to a wide range of fields, with the expectation that significant contributions could be made to reducing methane emissions and combating climate change. Full article

Fluid components in cores are crucial parameters in evaluating the quality of a shale reservoir in both laboratory analyses and log interpretation. In the Gulong area, shale reservoirs are characterized by a high clay content, with clay spaces hosting both oil and water phases, complicating the occurrence mechanism of fluid components, as a result, traditional research methods are no longer applicable. As an advanced technique, nuclear magnetic resonance (NMR) has been applied in oilfields to determine the specific petrophysical properties of rocks. To more accurately identify the types of fluid components, this study carried out a new, well-designed 2D NMR experiment, rock pyrolysis experiment, and quantitative oil and water detection experiment (QOWDE) to study the Gulong shale. This study measured the 2D NMR map of the original state, saturation state, centrifugal state, and pyrolysis at different temperatures, and conducted mutual verification between the QOWDE and 2D NMR pyrolysis experiments to obtain the distribution of different components of Gulong shale on the 2D NMR map. Based on the experimental results, this study developed a component identification template suitable for the Gulong area and calculated the 2D NMR porosity and saturation from it. This lays a foundation for the analysis and application of fluid components in the Gulong region and provides a new experimental basis and methodological support for porosity and saturation calculations. Full article

Due to their high porosity and permeability characteristics, reservoirs of loose sandstone have great development potential. Under weak dynamic conditions, the vertical migration and mass exchange of oil–water two-phase fluids in loose sandstone reservoirs occur very easily. The phenomenon of vertical seepage flow equilibrium has a significant impact on the distribution of oil–water two-phase fluids in the reservoir. However, existing mainstream numerical simulators cannot accurately describe the phenomenon of vertical migration of oil–water two-phase fluids under weak dynamic conditions. In this study, using 3D printing technology, multiple transparent rock core holders were constructed to conduct experiments on the vertical seepage flow equilibrium time of different viscosities and contents of crude oil under different permeabilities of rock cores. Through the analysis and regression of experimental results, a predictive formula for the vertical seepage flow equilibrium time of loose sandstone reservoirs was established. Based on the time-prediction discriminant formula, a multi-scale numerical simulation method for vertical seepage flow equilibrium was constructed. A comparison between the new method and experimental results showed that the numerical simulation method, considering vertical seepage flow equilibrium, is closer to experimental phenomena than traditional numerical simulation methods. This indicates that the method can more accurately reveal the characteristics and distribution laws of the vertical seepage flow of oil–water two-phase fluids in loose sandstone reservoirs. Full article

Focusing on the problem of poor demulsification performance of light crude oil emulsions in low-permeability oilfields at low temperatures, the composition of the emulsion samples, clay particle size distribution, and the viscosity–temperature relationship curve of samples were analyzed. Based on the results of emulsion composition analysis and characteristics, the bottle test method was used to analyze the demulsifying effect of different commercial types of demulsifiers, revealing the demulsification mechanism. The field tests confirm the demulsification capabilities of Polyoxyethylene polyoxypropylene quaternized polyoxyolefins surfactants (PR demulsifiers). The results reveal that PR demulsifiers combine the features of decreasing the interfacial tension between oil and water and adsorbing SiO₂, allowing for quick demulsification and flocculation at low temperatures. This research serves as a theoretical and practical foundation for the study and advancement of low-temperature demulsification technology in oilfields. Full article

In order to develop the biological self-cleaning anti-clogging high-permeability sand filter tube, it is very important to analyze the plugging material and plugging mechanism of crude oil sand water filter. Under laboratory conditions, based on vacuum drying, condensation reflux, chromatographic separation, and other technologies, the plugging components were preliminarily analyzed. The plugging components were analyzed via XRD, infrared analysis, ¹H NMR, and ¹³C NMR. Through analysis and testing, XRD results showed that the solid compositions were clay and sand grains. Meanwhile, the infrared analysis, ¹H NMR, and ¹³C NMR demonstrated that the main components of the plug are asphaltene crude oil, and the proportion of aromatic components and saturated components is close. Full article

Hexavalent chromium (Cr(VI)) is a typical heavy metal pollutant, making its removal from wastewater imperative. Although nanosized zero-valent iron (nZVI) and graphene-based materials are excellent remediation materials, they have drawbacks, such as agglomeration and being difficult to recycle. A facile synthesis method for decorating reduced graphene oxide (rGO) with ultrathin nZVI (within 10 nm) was explored in this study in order to develop an effective tool for Cr(VI) detoxication. Cu particles were doped in these composites for electron-transfer enhancement and were verified to improve the rate by 2.4~3.4 times. Batch experiments were conducted at different pHs, initial concentrations, ionic strengths, and humic acid (HA) concentrations. From these observations, it was found that the acid condition and appearance of Cu and rGO enhanced the treatment capacity. This procedure was fitted with a pseudo-second-order model, and the existence of NaCl and HA impeded it to some extent. Cr(VI) could be detoxified into Cr(III) and precipitated on the surface. Combining these analyses, a kinetics study, and the characterizations before and after the reaction, the removal mechanism of Cr(VI) was further discussed as a complex process involving adsorption, reduction, and precipitation. The maximum removal capacity of 156.25 mg g⁻¹ occurred in the acid condition, providing a potential Cr(VI) remediation method. Full article

To address the issue of pipeline blockage caused by the formation of waxy deposits inside pipelines, hindering the flow of petroleum in the Shengli oilfield, eight new-style polyacrylic acid pour point depressants (PPD) for Shengli crude oil were prepared by maleic anhydride and ene monomers with different polar and aromatic pendant chains. The synthesized Pour Point Depressants were characterized by Fourier transform infrared spectroscopy (FTIR), nuclear magnetic resonance (NMR), gel permeation chromatography (GPC), and polarizing optical microscopy (POM). The results were promising and demonstrated that any type of pour point depressant exhibited excellent performance on high-pour-point crude oil. The reduction in pour-point after additive addition was largely dependent on the polymer structure. Notably, polymers containing long alkyl side chains and aromatic units displayed the most impressive performance, capable of depressing the pour point by 12 °C. Full article

Training images are important input parameters for multipoint geostatistical modeling, and training images that can portray 3D spatial correlations are required to construct 3D models. The 3D training images are usually obtained by unconditional simulation using algorithms such as object-based algorithms, and in some cases, it is difficult to obtain the 3D training images directly, so a series of modeling methods based on 2D training images for constructing 3D models has been formed. In this paper, a new modeling method is proposed by synthesizing the advantages of the previous methods. Taking the fluvial reservoir modeling of the P oilfield in the Bohai area as an example, a comparative study based on 2D and 3D training images was carried out. By comparing the variance function, horizontal and vertical connectivity in x-, y-, and z-directions, and style similarity, the study shows that based on several mutually perpendicular 2D training images, the modeling method proposed in this paper can achieve an effect similar to that based on 3D training images directly. In the case that it is difficult to obtain 3D training images, the modeling method proposed in this paper has suitable application prospects. Full article

The knowledge of the spatial distribution, sources, and air–soil exchange of polycyclic aromatic compounds (PACs) in an oilfield area is essential to the development of effective control practices of PAC pollution. In this study, 48 passive air samples and 24 soil samples were collected during 2018–2019 in seven functional areas (e.g., urban, oil field, suburban, industrial, agricultural, near pump units, and background) in the Yellow River Delta (YRD) where the Shengli Oilfield is located, and 18 parent polycyclic aromatic hydrocarbons (PAHs) and five alkylated-PAHs (APAHs) were analyzed from all the air and soil samples. The ΣPAHs in the air and soil ranged from 2.26 to 135.83 ng/m³ and 33.96 to 408.94 ng/g, while the ΣAPAHs in the atmosphere and soil ranged from 0.04 to 16.31 ng/m³ and 6.39 to 211.86 ng/g, respectively. There was a downward trend of atmospheric ΣPAH concentrations with increasing the distance from the urban area, while both ΣPAH and ΣAPAH concentrations in the soil decreased with distance from the oilfield area. PMF analyses show that for atmospheric PACs, coal/biomass combustion was the main contributor in urban, suburban, and agricultural areas, while crude production and processing source contributes more in the industrial and oilfield area. For PACs in soil, densely populated areas (industrial, urban, and suburban) are more affected by traffic sources, while oilfield and near-pump unit areas are under the impact of oil spills. The fugacity fraction (${\mathrm{f}}_{\mathrm{f}}$) results indicated that the soil generally emitted low-molecular-weight PAHs and APAHs and act as a sink for high-molecular-weight PAHs. The incremental lifetime cancer risk (ILCR) of Σ(PAH+APAH) in both the air and soil, were below the threshold (≤10⁻⁶) set by the US EPA. Full article

Since the Industrial Revolution, methane has become the second most important greenhouse gas component after CO₂ and the second most important culprit of global warming, leading to serious climate change problems such as droughts, fires, floods, and glacial melting. While most of the methane in the atmosphere comes from emissions from energy activities such as petroleum refining, storage tanks are an important source of methane emissions during the extraction and processing of crude oil and natural gas. Therefore, the use of high-resolution remote sensing image data for oil and gas production sites to achieve efficient and accurate statistics for storage tanks is important to promote the strategic goals of “carbon neutrality and carbon peaking”. Compared with traditional statistical methods for studying oil storage tanks, deep learning-based target detection algorithms are more powerful for multi-scale targets and complex background conditions. In this paper, five deep learning detection algorithms, Faster RCNN, YOLOv5, YOLOv7, RetinaNet and SSD, were selected to conduct experiments on 3568 remote sensing images from five different datasets. The results show that the average accuracy of the Faster RCNN, YOLOv5, YOLOv7 and SSD algorithms is above 0.84, and the F1 scores of YOLOv5, YOLOv7 and SSD algorithms are above 0.80, among which the highest detection accuracy is shown by the SSD algorithm at 0.897 with a high F1 score, while the lowest average accuracy is shown by RetinaNet at only 0.639. The training results of the five algorithms were validated on three images containing differently sized oil storage tanks in complex backgrounds, and the validation results obtained were better, providing more accurate references for practical detection applications in remote sensing of oil storage tank targets in the future. Full article

In this study, a modified chemical plugging agent is prepared with the aim to reduce the well moisture content and improve the efficiency of oilfield development. In comparison to other chemical plugging agents, the composite gels plugging agents have excellent blocking capacity and erosion resistance. In this study, optimal conditions for the preparation of plugging agents were explored. The results showed that the performance of polyacrylamide-sericite (PAM-sericite) gel improved at a polymerization temperature of 60 °C, a crosslinker concentration of 0.5%, an initiator concentration of 0.75%, an acrylamide concentration of 10.0%, and a sericite concentration of 10.0%. The characterization of PAM-sericite gel showed a certain fold-like shape with a smoother surface, indicating that the doped sericite makes the plugging agent more compact and firm. It was also found that the blocking ratio of the plugging agent can potentially reach 99.5% after the addition of sericite. Moreover, failure stress of the skeleton structure and the water swelling degree were increased by 63.5% and 51.2%, respectively. Additionally, long-term stability, temperature resistance, pressure resistance and pressure stability also showed improvement to varying degrees. It was concluded that this gel has better stability against different kinds of salt solutions and is not affected by particle size. Full article