Research on thermal insulation materials properties under HTHP conditions for deep oil and gas reservoir rock ITP-Coring

Deep oil and gas reservoirs are under high-temperature conditions,but traditional coring methods do not consider temperature-preserved measures and ignore the influence of temperature on rock porosity and permeability,resulting in distorted resource assessments.The development of in situ temperaturepreserved coring(ITP-Coring)technology for deep reservoir rock is urgent,and thermal insulation materials are key.Therefore,hollow glass microsphere/epoxy resin thermal insulation materials(HGM/EP materials)were proposed as thermal insulation materials.The materials properties under coupled hightemperature and high-pressure(HTHP)conditions were tested.The results indicated that high pressures led to HGM destruction and that the materials water absorption significantly increased;additionally,increasing temperature accelerated the process.High temperatures directly caused the thermal conductivity of the materials to increase;additionally,the thermal conduction and convection of water caused by high pressures led to an exponential increase in the thermal conductivity.High temperatures weakened the matrix,and high pressures destroyed the HGM,which resulted in a decrease in the tensile mechanical properties of the materials.The materials entered the high elastic state at 150℃,and the mechanical properties were weakened more obviously,while the pressure led to a significant effect when the water absorption was above 10%.Meanwhile,the tensile strength/strain were 13.62 MPa/1.3%and 6.09 MPa/0.86%at 100℃ and 100 MPa,respectively,which meet the application requirements of the self-designed coring device.Finally,K46-f40 and K46-f50 HGM/EP materials were proven to be suitable for ITP-Coring under coupled conditions below 100℃ and 100 MPa.To further improve the materials properties,the interface layer and EP matrix should be optimized.The results can provide references for the optimization and engineering application of materials and thus technical support for deep oil and gas resource development.

Characteristics and Trends of Deep Oil and Gas Research in China (1984-2024)—Research from the Perspective of CiteSpace

Deep oil and gas refer to oil and gas resources buried at a significant depth below the surface. Compared with conventional oil and gas, deep oil and gas often face more complex geological conditions and technological challenges, therefore, the development and exploitation of these oil and gas resources require advanced technology and equipment. Use bibliometrics to study academic literature. Select available data and download it in “RefWorks” format. Import the data into Cite Space 6.3.R2 software for author collaboration and keyword emergence analysis and visualization. Use Microsoft Excel 2016 software to analyze the annual publication volume, literature institutions, and disciplinary distribution of domestic and international scholarly literature. Research has found that: 1) The institution with the highest number of publications in the field of deep oil and gas in China is the China Petroleum Exploration and Development Research Institute;The author with the highest number of publications is Zhu Guangyou;The author with the highest citation frequency is Jia Chengzao;The research work in the field of deep oil and gas in China is mainly led by national level fund projects. 2) The research hot-spots of deep oil and gas in China are showing a trend of shifting from Jilin and Henan to Xinjiang and Sichuan. 3) The research on deep oil and gas fields in the Paleogene of China is mainly concentrated in Henan Province and Shandong Province. The Lower Tertiary, Cambrian and Jurassic are respectively concentrated in Dongpu Sag, Dongying Sag, Sichuan Basin, Tarim Basin in Xinjiang, the Junggar Basin and Qaidam Basin in Qinghai. The Sinian, Ordovician, Cretaceous, and Neogene systems are mainly concentrated in Sichuan, Xinjiang, and Qinghai provinces. The Permian system is mainly located in the southwest and Northwest of China. This article uses a new research perspective and methodology to systematically analyze the current situation and future development trends of deep oil and gas exploration and development in China, which is of great significance for promoting effective exploration and development of deep oil and gas resources.

Research advances on the mechanisms of reservoir formation and hydrocarbon accumulation and the oil and gas development methods of deep and ultra-deep marine carbonates

Based on the new data of drilling, seismic, logging, test and experiments, the key scientific problems in reservoir formation, hydrocarbon accumulation and efficient oil and gas development methods of deep and ultra-deep marine carbonate strata in the central and western superimposed basin in China have been continuously studied.(1) The fault-controlled carbonate reservoir and the ancient dolomite reservoir are two important types of reservoirs in the deep and ultra-deep marine carbonates. According to the formation origin, the large-scale fault-controlled reservoir can be further divided into three types:fracture-cavity reservoir formed by tectonic rupture, fault and fluid-controlled reservoir, and shoal and mound reservoir modified by fault and fluid. The Sinian microbial dolomites are developed in the aragonite-dolomite sea. The predominant mound-shoal facies, early dolomitization and dissolution, acidic fluid environment, anhydrite capping and overpressure are the key factors for the formation and preservation of high-quality dolomite reservoirs.(2) The organic-rich shale of the marine carbonate strata in the superimposed basins of central and western China are mainly developed in the sedimentary environments of deep-water shelf of passive continental margin and carbonate ramp. The tectonic-thermal system is the important factor controlling the hydrocarbon phase in deep and ultra-deep reservoirs, and the reformed dynamic field controls oil and gas accumulation and distribution in deep and ultra-deep marine carbonates.(3) During the development of high-sulfur gas fields such as Puguang, sulfur precipitation blocks the wellbore. The application of sulfur solvent combined with coiled tubing has a significant effect on removing sulfur blockage. The integrated technology of dual-medium modeling and numerical simulation based on sedimentary simulation can accurately characterize the spatial distribution and changes of the water invasion front.Afterward, water control strategies for the entire life cycle of gas wells are proposed, including flow rate management, water drainage and plugging.(4) In the development of ultra-deep fault-controlled fractured-cavity reservoirs, well production declines rapidly due to the permeability reduction, which is a consequence of reservoir stress-sensitivity. The rapid phase change in condensate gas reservoir and pressure decline significantly affect the recovery of condensate oil. Innovative development methods such as gravity drive through water and natural gas injection, and natural gas drive through top injection and bottom production for ultra-deep fault-controlled condensate gas reservoirs are proposed. By adopting the hierarchical geological modeling and the fluid-solid-thermal coupled numerical simulation, the accuracy of producing performance prediction in oil and gas reservoirs has been effectively improved.

Dynamic simulation of differential accumulation history of deep marine oil and gas in superimposed basin:A case study of Lower Paleozoic petroleum system of Tahe Oilfield,Tarim Basin,NW China

According to the complex differential accumulation history of deep marine oil and gas in superimposed basins,the Lower Paleozoic petroleum system in Tahe Oilfield of Tarim Basin is selected as a typical case,and the process of hydrocarbon generation and expulsion,migration and accumulation,adjustment and transformation of deep oil and gas is restored by means of reservoine-forming dynamics simulation.The thermal evolution history of the Lower Cambrian source rocks in Tahe Oilfield reflects the obvious differences in hydrocarbon generation and expulsion process and intensity in different tectonic zones,which is the main reason controlling the differences in deep oil and gas phases.The complex transport system composed of strike-slip fault and unconformity,etc.controlled early migration and accumulation and late adjustment of deep oil and gas,while the Middle Cambrian gypsum-salt rock in inner carbonate platform prevented vertical migration and accumulation of deep oil and gas,resulting in an obvious"fault-controlled"feature of deep oil and gas,in which the low potential area superimposed by the NE-strike-slip fault zone and deep oil and gas migration was conducive to accumulation,and it is mainly beaded along the strike-slip fault zone in the northeast direction.The dynamic simulation of reservoir formation reveals that the spatio-temporal configuration of"source-fault-fracture-gypsum-preservation"controls the differential accumulation of deep oil and gas in Tahe Oilfield.The Ordovician has experienced the accumulation history of multiple periods of charging,vertical migration and accumulation,and lateral adjustment and transformation,and deep oil and gas have always been in the dynamic equilibrium of migration,accumulation and escape.The statistics of residual oil and gas show that the deep stratum of Tahe Oilfield still has exploration and development potential in the Ordovician Yingshan Formation and Penglaiba Formation,and the Middle and Upper Cambrian ultra-deep stratum has a certain oil and gas resource prospect.This study provides a reference for the dynamic quantitative evaluation of deep oil and gas in the Tarim Basin,and also provides a reference for the study of reservoir formation and evolution in carbonate reservoir of paleo-craton basin.

Enhancing Deep Learning Soil Moisture Forecasting Models by Integrating Physics-based Models

Accurate soil moisture(SM)prediction is critical for understanding hydrological processes.Physics-based(PB)models exhibit large uncertainties in SM predictions arising from uncertain parameterizations and insufficient representation of land-surface processes.In addition to PB models,deep learning(DL)models have been widely used in SM predictions recently.However,few pure DL models have notably high success rates due to lacking physical information.Thus,we developed hybrid models to effectively integrate the outputs of PB models into DL models to improve SM predictions.To this end,we first developed a hybrid model based on the attention mechanism to take advantage of PB models at each forecast time scale(attention model).We further built an ensemble model that combined the advantages of different hybrid schemes(ensemble model).We utilized SM forecasts from the Global Forecast System to enhance the convolutional long short-term memory(ConvLSTM)model for 1–16 days of SM predictions.The performances of the proposed hybrid models were investigated and compared with two existing hybrid models.The results showed that the attention model could leverage benefits of PB models and achieved the best predictability of drought events among the different hybrid models.Moreover,the ensemble model performed best among all hybrid models at all forecast time scales and different soil conditions.It is highlighted that the ensemble model outperformed the pure DL model over 79.5%of in situ stations for 16-day predictions.These findings suggest that our proposed hybrid models can adequately exploit the benefits of PB model outputs to aid DL models in making SM predictions.

Tectonic Framework and Deep Structure of South China and Their Constraint to Oil-Gas Field Distribution

South China could be divided into one stable craton, the Yangtze Craton （YzC）, and several orogenic belts in the surrounding region, that is the Triassic Qinling-Dabie Orogenic Belt （QDOB） in the north, the Songpan-Garze Orogenic Belt （SGOB） in the northwest, the Mesozoic-Cenozoic Threeriver Orogenic Belt （TOB） in the west, the Youjiang Orogenic Belt （YOB） in the southwest, the Middle Paleozoic Huanan Orogenic Belt （HOB） in the southeast, and the Mesozoic-Cenozoic Maritime Orogenic Belt （MOB） along the coast. Seismic tomographic images reveal that the Moho depth is deeper than 40 km and the lithosphere is about 210 km thick beneath the YzC. The SGOB is characterized by thick crust （〉40 km） and thin lithosphere （〈150 km）. The HOB, YOB and MOB have a thin crust （〈40 km） and thin lithosphere （〈150 km）. Terrestrial heat flow survey revealed a distribution pattern with a low heat flow region in the eastern YzC and western HOB and two high heat flow regions in the TOB and MOB respectively. Such a ＂high-low-high＂ heat flow distribution pattern could have resulted from Cenozoic asthenosphere upwelling. All oil-gas fields are concentrated in the central part of the YzC. Remnant oil pools have been discovered along the southern margin of the YzC and its adjacent orogenic belts. From a viewpoint of geological and geophysical structure, regions in South China with thick lithosphere and low heat flow value, as well as weak deformation, might be the ideal region for further petroleum exploration.

Origin and accumulation of high-maturity oil and gas in deep parts of the Baxian Depression, Bohai Bay Basin, China

Great quantities of light oil and gas are produced from deep buried hill reservoirs at depths of 5,641 m to 6,027 m and 190 ℃ to 201 ℃ in the Niudong-1 Well, representing the deepest and hottest commercial hydrocarbons discovered in the Bohai Bay Basin in eastern China. This discovery suggests favorable exploration prospects for the deep parts of the basin. However, the discovery raises questions regarding the genesis and accumulation of hydrocarbons in deep reservoirs. Based on the geochemical features of the hydrocarbons and characteristics of the source rocks as well as thermal simulation experiments of hydrocarbon generation, we conclude that the oil and gas were generated from the highly mature Sha-4 Member （Es4） source rocks instead of thermal cracking of crude oils in earlier accumulations. The source kitchen with abnormal pressures and karsted carbonate reservoirs control the formation of high-maturity hydrocarbon accumulations in the buried hills （i.e., Niudong-1） in conjunction with several structural-lithologic traps in the ES4 reservoirs since the deposition of the upper Minghuazhen Formation. This means the oil and gas exploration potential in the deep parts of the Baxian Depression is probably high.

Fracture identification of carbonate reservoirs by deep forest model:An example from the D oilfield in Zagros Basin

Identifying fractures along a well trajectory is of immense significance in determining the subsurface fracture network distribution.Typically,conventional logs exhibit responses in fracture zones,and almost all wells have such logs.However,detecting fractures through logging responses can be challenging since the log response intensity is weak and complex.To address this problem,we propose a deep learning model for fracture identification using deep forest,which is based on a cascade structure comprising multi-layer random forests.Deep forest can extract complex nonlinear features of fractures in conventional logs through ensemble learning and deep learning.The proposed approach is tested using a dataset from the Oligocene to Miocene tight carbonate reservoirs in D oilfield,Zagros Basin,Middle East,and eight logs are selected to construct the fracture identification model based on sensitivity analysis of logging curves against fractures.The log package includes the gamma-ray,caliper,density,compensated neutron,acoustic transit time,and shallow,deep,and flushed zone resistivity logs.Experiments have shown that the deep forest obtains high recall and accuracy(>92%).In a blind well test,results from the deep forest learning model have a good correlation with fracture observation from cores.Compared to the random forest method,a widely used ensemble learning method,the proposed deep forest model improves accuracy by approximately 4.6%.

The deep structures and oil-gas prospect evaluation in the Qiangtang Basin, Tibet

The Qiantang Basin is now one of the topics of general interest in petroleum exploration in China. This paper reports a comprehensive study of geophysical and geological survey data recently obtained in this area and, combined with INDEPTH-3 deep survey results, comes to the following conclusions： 1） The hydrocarbon source formations, reservoirs, and overlying strata and their association within the basin are quite good, local structures are developed, and, therefore, the region is favorable for forming and preserving oil and gas accumulations. Faults are not a fatal problem. The future main target strata are the middle-deep structural strata composed of Upper-Triassic and middle Jurassic rocks; 2） A new classification has been made for second-order tectonic sequences inside the basin to disavow the central Qingtang uplift. It is noted that the main structures at the surface are orientated NW-SE and the crustal structure can be described as three depressions, three risees, and one deep depression, of which the prospective zone with the most potential is the inner main subsided belt and its two sides; 3） Comparatively intensive interaction between the crust and mantle and volcanic and thermal activities in the northern basin play a very important role in petroleum evaluation. The southern deeper sedimentation and less thermal activity make this area a more perfect zone for oil exploration; 4） Currently, the most important objective is determining the physical properties of the deep strata, the status of oil and gas accumulations, the source of the hydrocarbons, and the relationship between the upper and lower structures; and 5） The Lunpola Tertiary basin may be favorable for oil accumulations because petroleum may migrate from marine strata on two sides.

Insight into the experiment and extraction mechanism for separating carbazole from anthracene oil with quaternary ammonium-based deep eutectic solvents

Carbazole is an irreplaceable basic organic chemical raw material and intermediate in industry.The separation of carbazole from anthracene oil by environmental benign solvents is important but still a challenge in chemical engineering.Deep eutectic solvents (DESs) as a sustainable green separation solvent have been proposed for the separation of carbazole from model anthracene oil.In this research,three quaternary ammonium-based DESs were prepared using ethylene glycol (EG) as hydrogen bond donor and tetrabutylammonium chloride (TBAC),tetrabutylammonium bromide or choline chloride as hydrogen bond acceptors.To explore their extraction performance of carbazole,the conductor-like screening model for real solvents (COSMO-RS) model was used to predict the activity coefficient at infinite dilution (γ^(∞)) of carbazole in DESs,and the result indicated TBAC:EG (1:2) had the stronger extraction ability for carbazole due to the higher capacity at infinite dilution (C^(∞)) value.Then,the separation performance of these three DESs was evaluated by experiments,and the experimental results were in good agreement with the COSMO-RS prediction results.The TBAC:EG (1:2) was determined as the most promising solvent.Additionally,the extraction conditions of TBAC:EG (1:2) were optimized,and the extraction efficiency,distribution coefficient and selectivity of carbazole could reach up to 85.74%,30.18 and 66.10%,respectively.Moreover,the TBAC:EG (1:2) could be recycled by using environmentally friendly water as antisolvent.In addition,the separation performance of TBAC:EG (1:2) was also evaluated by real crude anthracene,the carbazole was obtained with purity and yield of 85.32%,60.27%,respectively.Lastly,the extraction mechanism was elucidated byσ-profiles and interaction energy analysis.Theoretical calculation results showed that the main driving force for the extraction process was the hydrogen bonding ((N–H...Cl) and van der Waals interactions (C–H...O and C–H...π),which corresponding to the blue and green isosurfaces in IGMH analysis.This work presented a novel method for separating carbazole from crude anthracene oil,and will provide an important reference for the separation of other high value-added products from coal tar.

Oxidative Desulfurization of Fuel Oil with H_(3)PO_(4)-based Deep Eutectic Solvents

A series of Lewis-acid deep eutectic solvents (DESs) were synthesized by stirring phosphoric acid and zincchloride as raw materials at 80℃ to form H_(3)PO_(4)/nZnCl_(2) (n = 0.1, 0.25, 0.5, 0.75, 1). The DESs were characterized byFourier transform infrared spectrophotometry (FT-IR), thermogravimetry/differential thermogravimetry (TG/DTG), andelectron spray ionization mass spectrometry (ESI-MS). The DESs were used as both extractants and catalysts to removedibenzothiophene from fuels via oxidative desulfurization (ODS). Experiments were performed to investigated the influenceof factors such as composition of DES, temperature, oxidant dosage (molar ratio of O:S), DES dosage (volume ratio ofDES:oil), and number of cycles on desulfurization rate. The results indicated that the removal rate of dibenzothiophene (DBT)was affected by the Lewis acidic DESs, with that of H_(3)PO_(4)/0.25∙ZnCl_(2) reaching 96.4% under optimal conditions (Voil=5 mL,VDES=1 mL, an oxidant dosage of 6, T=50 ℃). After six cycles, the desulfurization rate of H_(3)PO_(4)/0.25∙ZnCl_(2) remained above94.1%. The apparent activation energy of dibenzothiophene (DBT) removal reaction was determined by a pseudo-first orderkinetic equation according to the Arrhenius equation to be 32.34 kJ/mol, as estimated. A reaction mechanism is proposedbased on the experimental data and characterization results.

Review of Artificial Intelligence for Oil and Gas Exploration: Convolutional Neural Network Approaches and the U-Net 3D Model

Deep learning, especially through convolutional neural networks (CNN) such as the U-Net 3D model, has revolutionized fault identification from seismic data, representing a significant leap over traditional methods. Our review traces the evolution of CNN, emphasizing the adaptation and capabilities of the U-Net 3D model in automating seismic fault delineation with unprecedented accuracy. We find: 1) The transition from basic neural networks to sophisticated CNN has enabled remarkable advancements in image recognition, which are directly applicable to analyzing seismic data. The U-Net 3D model, with its innovative architecture, exemplifies this progress by providing a method for detailed and accurate fault detection with reduced manual interpretation bias. 2) The U-Net 3D model has demonstrated its superiority over traditional fault identification methods in several key areas: it has enhanced interpretation accuracy, increased operational efficiency, and reduced the subjectivity of manual methods. 3) Despite these achievements, challenges such as the need for effective data preprocessing, acquisition of high-quality annotated datasets, and achieving model generalization across different geological conditions remain. Future research should therefore focus on developing more complex network architectures and innovative training strategies to refine fault identification performance further. Our findings confirm the transformative potential of deep learning, particularly CNN like the U-Net 3D model, in geosciences, advocating for its broader integration to revolutionize geological exploration and seismic analysis.

Enrichment Mechanism and Prospects of Deep Oil and Gas

With the deepening of oil and gas exploration,the importance of depth is increasingly highlighted.The risk of preservation of storage space in deep reservoirs is greater than that in shallow and medium layers.Deep layers mean older strata,more complex structural evolution and more complex hydrocarbon accumulation processes,and even adjustment and transformation of oil and gas reservoirs.This paper systematically investigates the current status and research progress of deep oil and gas exploration around the world and looks forward to the future research focus of deep oil and gas.In the deep,especially the ultra-deep layers,carbonate reservoirs play a more important role than clastic rocks.Karst,fault-karst and dolomite reservoirs are the main types of deep and ultra-deep reservoirs.The common feature of most deep large and medium-sized oil and gas reservoirs is that they formed in the early with shallow depth.Fault activity and evolution of trap highs are the main ways to cause physical adjustment of oil and gas reservoirs.Crude oil cracking and thermochemical sulfate reduction(TSR)are the main chemical modification effects in the reservoir.Large-scale high-quality dolomite reservoirs is the main direction of deep oil and gas exploration.Accurate identification of oil and gas charging,adjustment and reformation processes is the key to understanding deep oil and gas distribution.High-precision detection technology and high-precision dating technology are an important guarantee for deep oil and gas research.

A deep reinforcement learning approach to gasoline blending real-time optimization under uncertainty

The gasoline inline blending process has widely used real-time optimization techniques to achieve optimization objectives,such as minimizing the cost of production.However,the effectiveness of real-time optimization in gasoline blending relies on accurate blending models and is challenged by stochastic disturbances.Thus,we propose a real-time optimization algorithm based on the soft actor-critic(SAC)deep reinforcement learning strategy to optimize gasoline blending without relying on a single blending model and to be robust against disturbances.Our approach constructs the environment using nonlinear blending models and feedstocks with disturbances.The algorithm incorporates the Lagrange multiplier and path constraints in reward design to manage sparse product constraints.Carefully abstracted states facilitate algorithm convergence,and the normalized action vector in each optimization period allows the agent to generalize to some extent across different target production scenarios.Through these well-designed components,the algorithm based on the SAC outperforms real-time optimization methods based on either nonlinear or linear programming.It even demonstrates comparable performance with the time-horizon based real-time optimization method,which requires knowledge of uncertainty models,confirming its capability to handle uncertainty without accurate models.Our simulation illustrates a promising approach to free real-time optimization of the gasoline blending process from uncertainty models that are difficult to acquire in practice.

A novel framework for predicting non-stationary production time series of shale gas based on BiLSTM-RF-MPA deep fusion model

Shale gas, as an environmentally friendly fossil energy resource, has gained significant commercial development and shows immense potential. However, accurately predicting shale gas production faces substantial challenges due to the complex law of decline, nonlinear and non-stationary features in production data, which greatly repair the robustness of current models in predicting shale gas production time series. To address these challenges and improve accuracy in production forecasting, this paper introduces a novel and innovative approach: a hybrid proxy model that combines the bidirectional long short-term memory(BiLSTM) neural network and random forest(RF) through deep learning. The BiLSTM neural network is adept at capturing long-term dependencies, making it suitable for understanding the intricate relationships between input and output variables in shale gas production.On the other hand, RF serves a dual purpose: reducing model variance and addressing the concept drift problem that arises in non-stationary time series predictions made by BiLSTM. By integrating these two models, the hybrid approach effectively captures the inherent dependencies present in long and nonstationary production time series, thereby reducing model uncertainty. Furthermore, the combination of BiLSTM and RF is optimized using the recently-proposed marine predators algorithm(MPA) to fine-tune hyperparameters and enhance the overall performance of the proxy model. The results demonstrate that the proposed BiLSTM-RF-MPA model achieves higher prediction accuracy and demonstrates stronger generalization capabilities by effectively handling the complex nonlinear and non-stationary characteristics of shale gas production time series. Compared to other models such as LSTM, BiLSTM, and RF, the proposed model exhibits superior fitting and prediction performance, with an average improvement in performance indicators exceeding 20%. This innovative framework provides valuable insights for forecasting the complex production performance of unconventional oil and gas reservoirs, which sheds light on the development of data-driven proxy models in the field of subsurface energy utilization.

Surface wave inversion with unknown number of soil layers based on a hybrid learning procedure of deep learning and genetic algorithm

Surface wave inversion is a key step in the application of surface waves to soil velocity profiling.Currently,a common practice for the process of inversion is that the number of soil layers is assumed to be known before using heuristic search algorithms to compute the shear wave velocity profile or the number of soil layers is considered as an optimization variable.However,an improper selection of the number of layers may lead to an incorrect shear wave velocity profile.In this study,a deep learning and genetic algorithm hybrid learning procedure is proposed to perform the surface wave inversion without the need to assume the number of soil layers.First,a deep neural network is adapted to learn from a large number of synthetic dispersion curves for inferring the layer number.Then,the shear-wave velocity profile is determined by a genetic algorithm with the known layer number.By applying this procedure to both simulated and real-world cases,the results indicate that the proposed method is reliable and efficient for surface wave inversion.

Quantitative prediction model for the depth limit of oil accumulation in the deep carbonate rocks:A case study of Lower Ordovician in Tazhong area of Tarim Basin

With continuous hydrocarbon exploration extending to deeper basins,the deepest industrial oil accumulation was discovered below 8,200 m,revealing a new exploration field.Hence,the extent to which oil exploration can be extended,and the prediction of the depth limit of oil accumulation(DLOA),are issues that have attracted significant attention in petroleum geology.Since it is difficult to characterize the evolution of the physical properties of the marine carbonate reservoir with burial depth,and the deepest drilling still cannot reach the DLOA.Hence,the DLOA cannot be predicted by directly establishing the relationship between the ratio of drilling to the dry layer and the depth.In this study,by establishing the relationships between the porosity and the depth and dry layer ratio of the carbonate reservoir,the relationships between the depth and dry layer ratio were obtained collectively.The depth corresponding to a dry layer ratio of 100%is the DLOA.Based on this,a quantitative prediction model for the DLOA was finally built.The results indicate that the porosity of the carbonate reservoir,Lower Ordovician in Tazhong area of Tarim Basin,tends to decrease with burial depth,and manifests as an overall low porosity reservoir in deep layer.The critical porosity of the DLOA was 1.8%,which is the critical geological condition corresponding to a 100%dry layer ratio encountered in the reservoir.The depth of the DLOA was 9,000 m.This study provides a new method for DLOA prediction that is beneficial for a deeper understanding of oil accumulation,and is of great importance for scientific guidance on deep oil drilling.

Evaluation and Application of Flowback Effect in Deep Shale Gas Wells

The pivotal areas for the extensive and effective exploitation of shale gas in the Southern Sichuan Basin have recently transitioned from mid-deep layers to deep layers.Given challenges such as intricate data analysis,absence of effective assessment methodologies,real-time control strategies,and scarce knowledge of the factors influencing deep gas wells in the so-called flowback stage,a comprehensive study was undertaken on over 160 deep gas wells in Luzhou block utilizing linear flow models and advanced big data analytics techniques.The research results show that:(1)The flowback stage of a deep gas well presents the characteristics of late gas channeling,high flowback rate after gas channeling,low 30-day flowback rate,and high flowback rate corresponding to peak production;(2)The comprehensive parameter AcmKm1/2 in the flowback stage exhibits a strong correlation with the Estimated Ultimate Recovery(EUR),allowing for the establishment of a standardized chart to evaluate EUR classification in typical shale gas wells during this stage.This enables quantitative assessment of gas well EUR,providing valuable insights into production potential and performance;(3)The spacing range and the initial productivity of gas wells have a significant impact on the overall effectiveness of gas wells.Therefore,it is crucial to further explore rational well patterns and spacing,as well as optimize initial drainage and production technical strategies in order to improve their performance.

Relationship between Natural Fracture and Structural Style and its Implication for Tight Gas Enrichment:A Case Study of Deep Ahe Formation in the Dibei–Tuzi Area,Kuqa Depression

The deep Lower Jurassic Ahe Formation(J_(1a))in the Dibei–Tuzi area of the Kuqa Depression has not been extensively explored because of the complex distribution of fractures.A study was conducted to investigate the relationship between the natural fracture distribution and structural style.The J_(1a)fractures in this area were mainly high-angle shear fractures.A backward thrust structure(BTS)is favorable for gas migration and accumulation,probably because natural fractures are more developed in the middle and upper parts of a thick competent layer.The opposing thrust structure(OTS)was strongly compressed,and the natural fractures in the middle and lower parts of the thick competent layer around the fault were more intense.The vertical fracture distribution in the thick competent layers of an imbricate-thrust structure(ITS)differs from that of BTS and OTS.The intensity of the fractures in the ITS anticline is similar to that in the BTS.Fracture density in monoclinic strata in a ITS is controlled by faulting.Overall,the structural style controls the configuration of faults and anticlines,and the stress on the competent layers,which significantly affects deep gas reservoir fractures.The enrichment of deep tight sandstone gas is likely controlled by two closely spaced faults and a fault-related anticline.

Optimizing the Diameter of Plugging Balls in Deep Shale Gas Wells

Deep shale gas reserves that have been fractured typically have many relatively close perforation holes. Due to theproximity of each fracture during the formation of the fracture network, there is significant stress interference,which results in uneven fracture propagation. It is common practice to use “balls” to temporarily plug fractureopenings in order to lessen liquid intake and achieve uniform propagation in each cluster. In this study, a diameteroptimization model is introduced for these plugging balls based on a multi-cluster fracture propagationmodel and a perforation dynamic abrasion model. This approach relies on proper consideration of the multiphasenature of the considered problem and the interaction force between the involved fluid and solid phases. Accordingly,it can take into account the behavior of the gradually changing hole diameter due to proppant continuousperforation erosion. Moreover, it can provide useful information about the fluid-dynamic behavior of the consideredsystem before and after plugging. It is shown that when the diameter of the temporary plugging ball is1.2 times that of the perforation hole, the perforation holes of each cluster can be effectively blocked.