Oil Exploration Research Articles

Source-to-sink processes and genetic mechanism of progradational and lateral accretion submarine fans in the Qiongdongnan Basin, South China Sea

Submarine fan reservoirs are important accumulation zones for oil, gas, and natural gas hydrates, offering significant potential for hydrocarbon exploration. During the deposition period of the Sanya Formation in the southern part of the Changchang Sag of the Qiongdongnan Basin, a large submarine fan developed. However, the internal structure, source-sink system, and formation mechanism of this fan remain poorly understood, posing significant challenges to exploration in this area. This paper examines the source-to-sink sedimentary processes and deposition of submarine fans, using the Changchang Sag, in the Qiongdongnan Basin in the Northern South China Sea, as an example, which will provide valuable general guidance for deep water oil and gas exploration. Based on the theories of seismic stratigraphy and seismic sedimentology, this paper utilizes techniques such as seismic facies analysis, seismic attribute optimization, paleogeomorphology reconstruction, and source-to-sink sedimentary system analysis to analyze the 3D seismic data of the study area. Research indicates that the Sanya Formation in the Changchang Sag of the Qiongdongnan Basin comprises three depositional units: submarine fan, feeder channel, and Semi-deep marine to deep marine mudstone. The submarine fan is a fan formed by the coupling and convergence of submarine fans sourced from the southwest and southeast. Internally, it is divided into three sub-facies: the proximal fan of the sand-rich submarine fan, the main body of the sand-rich submarine fan lobes, and the distal lobes of the sand-rich submarine fan. The submarine fan sourced from the southwest extends nearly north-south and is primarily fed by sediment transported through three large, banded ancient valleys. The sedimentary filling is characterized by three-phase progradation. The submarine fan sourced from the southeast extends nearly east-west and is primarily fed by sediment transported through a single large, banded ancient valley. The sedimentary filling is characterized by two-phase lateral accumulation. During the deposition period of the Sanya Formation, certain areas of the southern uplift belt were exposed for extended periods and subjected to weathering and erosion. Sediments are transported to large ancient valleys through small supply channels. A large number of sediments were transported to the southern slope of the Changchang sag through the provenance channel system such as large ancient valleys and slope belts and deposited in the center of the sag. These make up a complete system of large ancient uplifts and submarine fan source-to-sink sedimentary systems.The sedimentary model is a lobed submarine fan controlled by semi-restricted ancient valleys and expansive basins.

Application of the wavelet transform and Hilbert–Huang transform in stratigraphic sequence division of Jurassic Shaximiao Formation in Southwest Sichuan Basin

Abstract In the southwestern Sichuan Basin, the Jurassic Shaximiao Formation encompasses a multitude of working areas, displaying intricate sedimentary traits. Traditional methods of stratigraphic division based on sequence suffer from inherent subjectivity and limitations. This study employs a combined mathematical approach to use the wavelet transform (WT) and the Hilbert–Huang transform (HHT). It decomposes the natural gamma ray (GR) logging curve into energy spectrum plots and wavelet coefficients at different scales, high and low frequency signals at different frequencies, and a set of intrinsic mode function components and residual functions. The study conducted a detailed stratigraphic division of the Jurassic Shaximiao Formation in the southwestern Sichuan Basin using these methods. The WT offers greater resolution for the periodic changes in the base level, whereas the HHT demonstrates a superior correlation with the positions of stratigraphic interfaces. The combined utilization of the continuous wavelet transform, the discrete wavelet transform, and HHT methods has demonstrated encouraging outcomes in the stratigraphic division of the Jurassic Shaximiao Formation. These methods have been shown to enhance the accuracy of stratigraphic division and to reduce the influence of subjective factors. This study presents new insights and approaches for geological data processing, offering significant theoretical and practical implications and novel technical means for oil and gas exploration and development.

Investigation on the fatigue properties of adhesives for acoustic transducers in deep logging wells

Abstract Deep well exploration of oil and gas resources requires acoustic transducers that can withstand temperatures above 200 degrees. It is particularly important to overcome the key technology of high-temperature dipole transmitters. The bonding strength of the dipole transmitters with a tri-laminar structure is studied numerically and experimentally. Transducers were prepared using structural adhesives. The aging and fatigue test is conducted at a high temperature of 200 degrees. The aging lasted for 15 hours and the strong electric field fatigue cycle number reached up to 36000. The conductivity curve remains almost unchanged after the high-temperature test. The work provides an application basis for deep acoustic logging technology.

Rhamnolipids Enhance Bioremediation of Petroleum-Contaminated Soil

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 215952, “Enhancing Bioremediation of Petroleum-Contaminated Soil Using Rhamnolipids: A Combined Laboratory and Field Study,” by Pan Ni, Lehigh University; Yonglin Ren, SPE, Stepan Oilfield Solutions; and Derick G. Brown, Lehigh University, et al. The paper has not been peer reviewed. _ Hydrocarbon spills can occur at various stages of the oil and gas exploration and production process. Treating these spills onsite to avoid more-expensive excavation and incineration processes would be beneficial. The study outlined in the complete paper aims to optimize the use of rhamnolipid biosurfactants for enhancing the bioremediation of hydrocarbon-contaminated soil. The goal of this work was to explore the effects of rhamnolipid application on hydrocarbon degradation rate under both laboratory and field conditions and to examine the effects of this treatment on the indigenous soil microorganism population. Introduction The authors write that, to the best of their knowledge, previous research studies on rhamnolipid-based soil remediation focused on either laboratory or field tests and that comparison between laboratory-scale and field-scale tests is overlooked but necessary for practical applications. Materials and Methods Materials and Chemicals. The soil used for this work (23 kg for the laboratory test, 10 metric tons for the field test) was obtained from a contaminated site in Longford Mills, Canada. The soil properties are presented in Table 1 of the complete paper. The rhamnolipid stock solution featured 9.8 wt% rhamnolipid blends in deionized water. Another commercial remediation agent (enzyme-based) was used in the field test to compare with the performance of rhamnolipid. Ammonium chloride (NH4Cl) and potassium hydrogen phosphate (K2HPO4) also were used along with sodium hydroxide (NaOH). Experimental Setup. Both laboratory and field tests were ex-situ tests using simulated soil piles. The laboratory test was conducted for 193 days. For the laboratory experiments, an aerobic/anaerobic respirometer system equipped with a low-temperature incubator was used to monitor the in-situ oxygen uptake inside eight reactors (Fig. 1). Each reactor contained 250 g of soil with rhamnolipid doses of 0, 0.1, 0.5, and 1 g/kg. The temperature was set to 25°C. Inside each reactor was placed 10 mL of 4-M NaOH in a 20-mL glass beaker to adsorb the produced CO2. A parallel set of eight reactors was prepared and operated under the same conditions. The nutrients of nitrogen and phosphorous (NH4Cl and K2HPO4) were added at specific times based on the oxygen uptake rate monitored by the respirometer. To avoid the possible inhibition of the microbes in the soil as a result of high ionic strength (greater than 160 mM) in the soil pore water, NH4Cl and K2HPO4 were added so that the molar concentration in the soil pore water did not exceed 100 mM. The field test is being conducted in Longford Mills, Ontario, Canada, and is ongoing at the time of writing. For the field test, 10 metric tons of soil were mixed to achieve homogeneity before the preparation of the soil piles. Five different treatment conditions were examined using duplicate soil piles (10 piles total), with each consisting of 1 metric ton of soil. Nutrient addition was similar to that used in the laboratory tests. Temperature and moisture were monitored every few days at five points in each soil pile, and the pH was monitored once every month at five points.

High‐Resolution Ensemble Modelling of Coral Distributions in the Northern Gulf of Mexico Based on Geomorphometry: Coral Diversity and Benthic Habitat Fragmentation From Oil and Gas Infrastructure to Inform Spatial Planning

ABSTRACTThe northern Gulf of Mexico is home to several species of corals that provide a wide range of ecosystem services to other organisms. Oil and gas infrastructure, such as platforms and pipelines, form an extensive network throughout the northern Gulf of Mexico. Detrimental impacts associated with oil and gas exploration and extraction have been recorded in this area at depths where corals are found. Due to these ecosystems' vulnerability to long‐term impacts, it is necessary to determine areas of interest that would benefit from further exploration and informed spatial planning. This study aimed to identify potential areas of interest for coral studies in the northern Gulf of Mexico. Ensemble species distribution models for 13 species of corals including scleractinians, black corals, and octocorals were produced based on seafloor characteristics and combined to identify areas with relatively higher coral diversity potential than others. The ensemble modelling approach produced robust outputs, as evaluated by the area under the curve, Cohen's kappa coefficient, sensitivity, specificity and the proportion of correct predictions. The proximity of suitable habitat to active and proposed oil and gas infrastructure was evaluated; this spatial analysis showed that oil and gas infrastructures potentially impact 23.5% of all predicted suitable coral habitat in the study area and contribute to benthic habitat fragmentation. Twelve areas of interest greater than 100 km2 and located outside a 4‐km zone of potential influence from oil and gas infrastructure were delineated and deemed of interest for further exploration and spatial planning, and hypothetical prioritization scenarios for spatial planning are presented. The maps produced can inform discussions among stakeholders to reach the best spatial planning outcomes while considering other ecological, social, economic and governance factors.

Two marine sulfur-reducing bacteria co-culture is essential for productive infection by a T4-like Escherichia coli-infecting phage

The control of microbiologically influenced corrosion (MIC) challenges the oil exploration sector. The MIC results from electrochemical reactions facilitated by microorganisms such as sulfate-reducing bacteria (SRB), which adhere to the surface of the ducts forming biofilms. SRB uses sulfate as the final electron acceptor, resulting in hydrogen sulfide as the final product, a highly reactive corrosive, and toxic compound. Due to the high diversity of the SRB group, this study evaluated the effect of an Escherichia coli phage, with biofilm degrading enzymes, in preventing biofilm formation by microbial consortium P48SEP and reducing H2S production in a complex SRB community. Three phage concentrations were evaluated (104, 108 and 1012 UFP/ml). High and medium phage concentrations prevented biofilm development, as evidenced by scanning electron microscopy, chemical analysis, and cell counts. In addition, the virus altered the expression pattern of some bacterial genes and the relative abundance of proteins related to biofilm formation and cell stress response. Using a complex culture formed mainly by SRB, it was possible to observe the bacterial growth, H2S, and metabolic activity reduction after the phage was added. This study shows for the first time the ability of an E. coli-infecting phage to prevent the biofilm formation of an SRB consortium and infect and replicate at high concentrations on the non-specific host. This new finding turns the use of non-specific phages a promising alternative for the control of biocorrosion in oil and gas installations, on the other side, alert to the use of large concentration of phages and the influence on bacterial groups with geological importance, opening a research field in phage biology.

Characterization of fault-karst reservoirs based on deep learning and attribute fusion

AbstractThe identification of fault-karst reservoir is crucial for the exploration and development of fault-controlled oil and gas reservoirs. Traditional methods primarily rely on well logging and seismic attribute analysis for karst cave identification. However, these methods often lack the resolution needed to meet practical demands. Deep learning methods offer promising solutions by effectively overcoming the complex response characteristics of seismic wave fields, owing to their high learning capabilities. Therefore, this research proposes a method for fault-karst reservoir identification. Initially, a comparative analysis between the improved U-Net++ network and traditional deep convolutional networks is conducted to select appropriate training parameters for separate training of karst caves and faults. Subsequently, the trained models are applied to actual seismic data to predict karst caves and faults within the research area, followed by attribute fusion to acquire data on fault-karst reservoirs. The results indicate that: (1) The proposed method effectively identifies karst caves and faults, outperforming traditional seismic attribute and coherence methods in terms of identification accuracy, and slightly surpassing U-Net and FCN; (2) The fusion of predicted karst caves and faults yields clear delineation of the relationship between top karst caves and bottom fractures within the research area. In summary, the proposed method for fault-karst reservoirs identification and characterization provides valuable insights for the exploration and development of fault-controlled oil and gas reservoirs in the region.

Sedimentary dynamics and depositional model for mud accumulation in deep lake basins: A case study in the Upper Triassic Chang-7 Member, Ordos Basin, northern China

Shales in deep lake basins have become the main focus of continental shale oil and gas exploration. In order to highlight the sedimentary dynamics of mud deposition in deep lake basins, a combination of core observation, thin section examination, X-ray diffraction, and QEMSCAN (quantitative evaluation of minerals by scanning electron microscopy) was used to analyze the depositional characteristics of mudrocks in the Chang-7 Member from the Yanchang Formation (Upper Triassic) in Ordos Basin, and to establish a depositional model for mud accumulation in deep lake basins. This study recognizes four mudrock lithofacies in the Chang-7 Member: (1) the laminated silt-bearing mudstone, which generally develops a binary composition of “silt–clay” or a ternary composition of “silt–clay–organic matter”; (2) the graded mudstone, mainly composed of dark gray and gray-black mudstone sandwiched by silt-bearing mudstone; (3) the massive mudstone, internally showing a uniform distribution of quartz, clay, and carbonate minerals, with also a small amount of organic detritus; and (4) the laminated shale, which is generally composed of clay laminae, and organic laminae of the former two. Sediment supply, topographic slope, and flood intensity combine to control the evolution of gravity flows and the transport and deposition of the mudrock in the Chang-7 Member. The influence of orogeny provides terrain gradient, water depth, abundant sediments at source areas, and triggering mechanism for the formation of gravity flows. Floods triggered by wetting events provide the impetus for sediment transport. Mud deposition in the Chang-7 Member was mainly related to the transport and sedimentation of mud by hyperpycnal flows and rapid sedimentation by buoyant plume flocculation. A comprehensive evolutionary model for shale accumulation in the deep lake basin is established by integrating various triggering mechanisms and mud transport sedimentary processes.

An eco-friendly approach to separate emulsified oil from water using all natural materials of chitosan and beach sand

Oil-contaminated water from oil and gas exploration remains the industry’s primary waste stream. The common method of using chemical coagulation/flocculation followed by air flotation has drawbacks such as generating non-biodegradable and toxic sludge and high operational costs. This study presents an eco-friendly alternative utilizing chitosan and beach sand to remove emulsified oil from water. Chitosan acts as a biodegradable flocculant, while beach sand aids in high-density floc formation and accelerates settling velocity. This approach achieved up to 94 % oil removal efficiency and reduced settling time from 90 to 15 min by using 100 mg/L chitosan and 500 mg/L beach sand with a particle size distribution of 50–100 μm. Shorter settling time reduces capital expenditure compared to conventional methods. Additionally, using natural materials like chitosan and beach sand minimizes toxic sludge generation. This eco-friendly approach offers a promising alternative to conventional methods for treating oily wastewater.

Spatial distribution of PAHs, nickel, and vanadium in sediments from a large coastal lagoon near a petroleum extraction area in the southern Gulf of Mexico

One of the world's crucial areas for crude oil exploration and extraction is the southern Gulf of Mexico, where Terminos Lagoon (TL) is located. Sediments from the TL region were used to assess the spatial patterns, origins, and ecotoxicological risks associated with 16 priority polycyclic aromatic hydrocarbons (PAHs; 3.1–248.9 ng⸳g−1 dry weight basis, dw) and trace metals (Ni = 11.0–104.0 mg⸳kg−1; V = 2.0–35.0 mg⸳kg−1 dw) linked to anthropogenic activities. Although origin indices based on PAHs and metals concentrations indicate no crude oil pollution in the region, sources of pyrogenic PAHs were identified. A chemometric approach demonstrated associations between organic matter and PAHs, and that metal accumulation depends mostly by the input of lithogenic materials. Ecotoxicological risk estimations showed a higher risk of possible adverse effects in sites near swamps and mangrove zones, highlighting the need of future monitoring. This study provides a reference for policymakers to conserve Mexico's largest coastal lagoon and other oil-impacted coastal areas worldwide.

Maritime law enforcement concerning offshore energy platforms: Navigating international law constraints and challenges

Offshore energy platforms (OEPs) serve as a crucial frontier in utilizing renewable energy sources to meet energy demands, powering essential maritime operations such as remote island power supply, offshore oil and gas exploration, seawater desalination, and marine renewable energy harvesting. However, the development of OEPs raises significant legal concerns for maritime law enforcement. Employing a policy analysis approach, this research delves into the multifaceted challenges faced by the current international legal framework in regulating enforcement activities pertaining to OEPs. These challenges encompass ambiguity in OEP legal status, difficulties in balancing safety measures with diverse maritime interests, obstacles in environmental assessment and enforcement, and heightened enforcement challenges in areas beyond national jurisdiction. The study explores potential strategies for resolution and collaboration on an international scale. Through this investigation, the study aims to provide valuable insights to promote greater clarity and coherence in maritime law enforcement practices concerning OEPs, thereby enhancing safety, security, and environmental sustainability for OEP operations in maritime environments globally.

Study on the relationship between shale organic matter development and paleowater depth—A new understanding of the condensed section

Recent studies dispute the long-standing belief that highly organic-rich shales are predominantly linked to condensed section (CS) at the maximum flooding surface (MFS), proposing instead that they more commonly develop in shallow waters along basin margins. This hypothesis, however, has not been thoroughly investigated. Our study, focusing on the pivotal regions of the Longmaxi Formation and the Chang 7 Member for marine and continental shale exploration in China, integrates sequence stratigraphy with analyses of total organic carbon (TOC) and elemental geochemistry to pinpoint the environments conducive to the formation of organic-rich shales. The most organic-rich intervals, specifically the bottom of the Longmaxi Formation and the mid-to-lower Chang 73 sub-member, are located within the transgressive systems tract (TST), showing higher TOC levels in the middle to lower TST, compared to the CS of the MFS. The formation of high organic matter shale is related to warm, humid, and dysoxic shallow waters with minimal terrigenous influx and high primary productivity. We found a pronounced inverse relationship between organic matter concentration and water depth, with deeper waters promoting the decomposition and recycling of organic matter during sedimentation, thereby diminishing the TOC content. Moreover, volcanic activity also affected the organic enrichment in the Chang 7 Member. With high primary productivity, organic matter tends to accumulate in dysoxic slope zones. Consequently, high-organic content shale intervals are generally situated in shallow, dysoxic water environments, suggesting that the CS of the MFS may actually represent organic-poor intervals with limited source rock potential. The results of this study are of great significance for shale oil and gas exploration in China, and provide a new direction for the discovery of shale oil and gas sweet spots.

Spatial nonstationarity and the role of environmental metal exposures on COVID-19 mortality in New Mexico

Worldwide, the COVID-19 pandemic has been influenced by a combination of environmental and sociodemographic drivers. To date, population studies have overwhelmingly focused on the impact of societal factors. In New Mexico, the rate of COVID-19 infection and mortality varied significantly among the state's geographically dispersed, and racially and ethnically diverse populations who are exposed to unique environmental contaminants related to resource extraction industries (e.g. fracking, mining, oil and gas exploration). By looking at local patterns of COVID-19 disease severity, we sought to uncover the spatially varying factors underlying the pandemic. We further explored the compounding role of potential long-term exposures to various environmental contaminants on COVID-19 mortality prior to widespread applications of vaccinations. To illustrate the spatial heterogeneity of these complex associations, we leveraged multiple modeling approaches to account for spatial non-stationarity in model terms. Multiscale geographically weighted regression (MGWR) results indicate that increased potential exposure to fugitive mine waste is significantly associated with COVID-19 mortality in areas of the state where socioeconomically disadvantaged populations were among the hardest hit in the early months of the pandemic. This relationship is paradoxically reversed in global models, which fail to account for spatial relationships between variables. This work contributes both to environmental health sciences and the growing body of literature exploring the implications of spatial nonstationarity in health research.

Permeability prediction using logging data from tight reservoirs based on deep neural networks

Permeability is a critical parameter for evaluating reservoir properties, and accurate prediction is an important basis for identifying high-quality reservoirs and geological modeling. However, the strong heterogeneity, complex lithology and diagenesis in the reservoirs of this region pose a major challenge for the accurate assessment of reservoir permeability. In recent years, the use of machine learning (ML) to solve problems in geophysical well logging and related fields has gained much attention thanks to advances in data science and artificial intelligence. ML is any predictive algorithm or combination of algorithms that learns from data and makes predictions without being explicitly coded with a deterministic model. The most immediate example is deep neural networks (DNN) that are trained with data to minimize a cost function and make predictions. The tight reservoirs in the Chang 7 Member of the Ordos Basin host significant oil and gas resources and have recently emerged as the main focus of unconventional oil and gas exploration and development. In this work, we performed DNN-based permeability prediction for the tight reservoirs in the Ordos Basin area. From 19 well logs, we selected effective data points from 17 wells for DNN training after preprocessing and used the remaining two wells for testing. First, we trained the DNN with all collected parameters as inputs, resulting in permeability prediction R2 values of 0.64 and 0.72 for the two wells, indicating a good fit. We then optimized the input parameters by performing a crossplot analysis between these parameters and the permeability. Using the same network structure (with all hyperparameters set the same), we trained the DNN again to obtain a new DNN-based model. The prediction results showed that removing input parameters with poor correlation to permeability improved the prediction accuracy with R2 values of 0.70 and 0.87 for the two wells.

Effect of Quenching Post‐Intercritical Austenitizing on the Microstructure and Tensile Properties of an K55 Grade Steel for Oil and Gas Industry

The API K55 grade steel is widely utilized in seamless pipes for oil and gas exploration, especially as casing pipes for wellbores. Traditionally, this steel is processed using hot rolling followed by quenching and tempering to achieve the desired dimensional and microstructural characteristics, balancing high strength with ductility. This article introduces an alternative method to attaining the required tensile properties for API K55 grade steel by employing a biphasic microstructure (ferrite/martensite) achieved through quenching post‐intercritical austenitizing heat treatment to high‐strength‐low‐alloy steel. Thermodynamic simulations and dilatometric experiments revealed that increasing the austenitizing temperature enhances austenite formation, decreasing significantly its carbon content, which facilitates martensitic transformation and increases the Ms and Mf temperatures. A complete phase transformation mapping was presented, highlighting how the austenitizing temperature influences martensitic transformation kinetics during the quenching heat treatment. It was concluded that austenitizing at 750 °C, followed by quenching and short tempering at 650 °C, produced a biphasic microstructure with 30% ferrite and 70% martensite, providing a favorable balance between mechanical strength and ductility that meets the API K55 grade requirements, surpassing traditional methods in the industry.

Research on Nondestructive Testing Technology for Drilling Risers Based on Magnetic Memory and Deep Learning

Drilling risers play a crucial role in deepwater oil and gas development, and any compromise in their integrity can severely hinder the progress of drilling operations. In light of this, efficient and accurate nondestructive testing of drilling risers is paramount. However, existing inspection equipment falls short in both efficiency and accuracy, posing challenges to the sustainability of deepwater oil and gas exploration and development. To effectively assess the damage conditions of deepwater drilling risers, this study developed an inspection robot based on metal magnetic memory and researched intelligent defect recognition methods using computer vision. The robot can perform in situ inspections on drilling risers and has been successfully deployed for field application on a deepwater drilling platform. The application results demonstrate that this detection robot offers significant advantages regarding high reliability and detection efficiency. Utilizing data collected on-site, we constructed a dataset containing 1100 images that cover five typical types of defects in drilling risers, including pitting, groove corrosion, and wear. Based on this dataset, we proposed and trained a novel image classification model, SK-ConvNeXt-KAN. By deeply optimizing the ConvNeXt convolutional network incorporating the introduced SK attention module and replacing traditional linear classification layers with the KAN module, this model significantly enhanced its feature extraction capabilities and efficiency in handling complex nonlinear problems. Experimental results show that this model achieved an accuracy rate of 95.4% in identifying defects in drilling risers, which is significantly better than traditional methods. This achievement has dramatically improved the efficiency and accuracy of deepwater drilling riser inspections, providing robust technical support for deepwater oil and gas exploration and development sustainability.

Assessment of coastal landscape fragmentation and its driving factors based on optimal scale: A case study of the Yellow River Delta, China

Since the large-scale oil exploration and development in the 1960 s, the Yellow River Delta (YRD) has undergone rapid industrialization and urbanization. Concurrently, the regional landscape ecological structure has experienced significant changes, and the degree of landscape fragmentation has increased. Therefore, a comprehensive assessment of the spatiotemporal dynamics and formation mechanisms of landscape fragmentation is crucial for the protection and restoration of natural ecosystem in the YRD. This study constructs an evaluation index system for landscape fragmentation in the YRD from three perspectives: quantitative characteristics, shape features, and spatial distribution. By combining inflection point analysis, landscape area loss index, and geostatistical models, the optimal spatial scale of landscape fragmentation from 2000 to 2020 is determined. Additionally, the study analyzes the spatiotemporal changes in landscape fragmentation and employs the optimal parameters-based geographical detector (OPGD) model to reveal the formation mechanisms of spatial heterogeneity in landscape fragmentation. The study findings are as follows: (1) The optimal spatial resolution for analyzing landscape fragmentation in the YRD was 80 m, and the extent was 880 m. (2) Between 2000 and 2020, landscape fragmentation in the YRD increased, with the highest level recorded in 2020. Highly fragmented areas, particularly inland regions, expanded remarkably, whereas coastal areas experienced lower fragmentation levels. (3) Distance to rivers and ditch density were the primary factors affecting landscape fragmentation, followed by land-use type and average land gross domestic product. The influence of natural factors was relatively minimal. The interaction between factors had a greater influence on landscape fragmentation compared to individual factors. The distance to rivers and land-use type exhibited the most substantial enhancement effects following interaction with other factors. This study provides valuable insights into the spatiotemporal dynamics and mechanisms of landscape fragmentation in the YRD, providing valuable guidance for the sustainable management and ecological restoration of the region and other similar ecologically sensitive areas.

How does oil policy uncertainty influence resource rents? New empirical evidence from Organization of the Petroleum Exporting Countries

AbstractThe uncertainty surrounding oil‐related policies has raised concerns about its influence on revenues derived from resource extraction activities. In this view, the current study aims to investigate the nuanced relationship between oil policy uncertainty (OPU) and resource rents, focusing on oil rents (ORTs), natural gas rents (NRTs), and total resource rents (TRT). Analyzing data spanning from 1985 to 2019 across Organization of the Petroleum Exporting Countries, various econometric models including DOLS, FMOLS, and autoregressive distributed lag are employed to assess the impact of OPU on resource rents. The empirical findings reveal a significant negative effect of heightened OPU levels on resource rents, indicating a reduction in ORT, NRT, and TRT. This negative impact underscores the deterrence of long‐term investments in oil exploration and production due to regulatory unpredictability, leading to decreased revenues from oil extraction activities. Additionally, increased OPU contributes to heightened volatility in oil prices, disrupting the stability of resource rents. Furthermore, variables such as FDI inflow, inflation rate, and banking sector development exhibit positive relationships with resource rents, emphasizing their role in bolstering revenues derived from natural resources. The study's implications highlight the necessity for policymakers to address and mitigate OPU to foster stability and sustainable revenues within resource‐driven economies. This study contributes to the existing literature by offering empirical insights into the adverse impact of OPU on resource rents.

Geochemical Characteristics and Origin of Natural Gas in the Middle of Shuntuoguole Low Uplift, Tarim Basin: Evidence from Natural Gas Composition and Isotopes

Multiple types of reservoirs, including volatile oil reservoirs, condensate gas reservoirs, and dry gas reservoirs, have been discovered in ultra-deep layers buried at depths greater than 7500 m. Understanding the genetic types of natural gas is of utmost importance in evaluating oil and gas exploration potential. The cumulative proved reserves of the super deep layer in the Shuntuoguole low uplift area of the Tarim Basin exceed 1 × 108 t (oil equivalent). The origin, source, and accumulation characteristics of natural gas still remain a subject of controversy. By analyzing the composition and carbon isotope of natural gas, a detailed investigation was conducted to examine the unique geochemical and reservoir formation characteristics of the Ordovician ultra-deep natural gas within different fault zones in the middle region of the Shuntuoguole low uplift. It was determined that most of the natural gas in this area is displaying a characteristic of wet gas with a drying coefficient ranging from 0.41 to 0.99. The carbon isotope composition of methane in the gas reservoir shows relatively light values, ranging from −49.4‰ to −42‰. The carbon and hydrogen isotopes of the components are distributed in a positive order. The natural gas is oil type gas, which is derived from marine sapropelic organic matter and has a good correspondence with the lower Yuertusi formation. The maturity of natural gas in Shunbei No. 1 and No. 5 fault zones is about 1.0%, which is the associated gas of normal crude oil, while the maturity of No. 4 and No. 8 fault zones is higher than 1.0%, which is the mixture of kerogen pyrolysis gas and crude oil pyrolysis gas. The variations in the drying coefficient and carbon isotope composition of the natural gas provide evidence for the migration patterns within the Shuntuoguole low uplift central region. It indicates that the Shunbei No. 5 and No. 8 fault zones have likely migrated from south to north, while the No. 4 fault zone has migrated from the middle to both the north and south sides. These migration patterns are primarily controlled by high and steep strike-slip faults, which facilitate the vertical migration of natural gas along fault planes. Consequently, the gas accumulates in fractured and vuggy reservoirs within the Ordovician formation.

Logging Identification Method for Reservoir Facies in Fractured-Vuggy Dolomite Reservoirs Based on AI: A Case Study of Ediacaran Dengying Formation, Sichuan Basin, China

As an important target for deep to ultra-deep carbonate oil and gas exploration, Fractured-Vuggy dolomite reservoirs have strong heterogeneity. Accurate characterization of reservoir facies is crucial for their exploration and exploitation. Three methods, including the unsupervised intelligent clustering method of improved Fuzzy C-means clustering Algorithm Based on Density Sensitive Distance and Fuzzy Partrition (FCM-DSDFP), the fusion method of Principal Components Analysis (PCA) dimensionality reduction and noise reduction, and the principle of clustering feature analysis are applied to identify reservoir facies based on logging data. Based on the PCA method, the logging response characteristics of the reservoir facies are excavated, and the fusion characterization data of dimensionality reduction and noise reduction are extracted. The FCM-DSDFP unsupervised intelligent clustering method, a model that approximates the subsurface conditions is established, and the reliability of the model is tested according to the elbow rule and silhouette coefficient. Combining drilling core observation, Fractured-Vuggy type, partially cemented Fractured-Vuggy type, Pore-Vuggy type, Pore Type I, Pore Type II, Tight Type I, and Tight Type II are divided in the Dengying Formation 4th Member. Fractured-Vuggy type, partially cemented Fractured-Vuggy type, Pore-Vuggy Type I, Pore-Vuggy Type II, Pore Type I, Pore Type II, and Tight Type are divided in the Dengying Formation 2nd Member, respectively. Two methods were applied to verify the reservoir facies identification results based on intelligent algorithms. The first method is to compare the identification results with the reservoir facies types identified by core observations (Well PT103 and PS13). The second method is to verify the recognition results of intelligent algorithms by utilizing the relationship between reservoir facies types and bitumen. The test results show that the accuracy of the reservoir level identification is higher than 0.9, and the applicability is better than the commonly used algorithms such as FCM and K-means.