A review of reservoir damage during hydraulic fracturing of deep and ultra-deep reservoirs

Deep and ultra-deep reservoirs have gradually become the primary focus of hydrocarbon exploration as a result of a series of significant discoveries in deep hydrocarbon exploration worldwide.These reservoirs present unique challenges due to their deep burial depth(4500-8882 m),low matrix permeability,complex crustal stress conditions,high temperature and pressure(HTHP,150-200℃,105-155 MPa),coupled with high salinity of formation water.Consequently,the costs associated with their exploitation and development are exceptionally high.In deep and ultra-deep reservoirs,hydraulic fracturing is commonly used to achieve high and stable production.During hydraulic fracturing,a substantial volume of fluid is injected into the reservoir.However,statistical analysis reveals that the flowback rate is typically less than 30%,leaving the majority of the fluid trapped within the reservoir.Therefore,hydraulic fracturing in deep reservoirs not only enhances the reservoir permeability by creating artificial fractures but also damages reservoirs due to the fracturing fluids involved.The challenging“three-high”environment of a deep reservoir,characterized by high temperature,high pressure,and high salinity,exacerbates conventional forms of damage,including water sensitivity,retention of fracturing fluids,rock creep,and proppant breakage.In addition,specific damage mechanisms come into play,such as fracturing fluid decomposition at elevated temperatures and proppant diagenetic reactions at HTHP conditions.Presently,the foremost concern in deep oil and gas development lies in effectively assessing the damage inflicted on these reservoirs by hydraulic fracturing,comprehending the underlying mechanisms,and selecting appropriate solutions.It's noteworthy that the majority of existing studies on reservoir damage primarily focus on conventional reservoirs,with limited attention given to deep reservoirs and a lack of systematic summaries.In light of this,our approach entails initially summarizing the current knowledge pertaining to the types of fracturing fluids employed in deep and ultra-deep reservoirs.Subsequently,we delve into a systematic examination of the damage processes and mechanisms caused by fracturing fluids within the context of hydraulic fracturing in deep reservoirs,taking into account the unique reservoir characteristics of high temperature,high pressure,and high in-situ stress.In addition,we provide an overview of research progress related to high-temperature deep reservoir fracturing fluid and the damage of aqueous fracturing fluids to rock matrix,both artificial and natural fractures,and sand-packed fractures.We conclude by offering a summary of current research advancements and future directions,which hold significant potential for facilitating the efficient development of deep oil and gas reservoirs while effectively mitigating reservoir damage.

Genetic mechanism and main controlling factors of high-quality clastic rock reservoirs in deep and ultradeep layers:A case study of Oligocene Linhe Formation in Linhe Depression,Hetao Basin,NW China

Based on new data from cores,drilling and logging,combined with extensive rock and mineral testing analysis,a systematic analysis is conducted on the characteristics,diagenesis types,genesis and controlling factors of deep to ultra-deep abnormally high porosity clastic rock reservoirs in the Oligocene Linhe Formation in the Hetao Basin.The reservoir space of the deep to ultra-deep clastic rock reservoirs in the Linhe Formation is mainly primary pores,and the coupling of three favorable diagenetic elements,namely the rock fabric with strong compaction resistance,weak thermal compaction diagenetic dynamic field,and diagenetic environment with weak fluid compaction-weak cementation,is conducive to the preservation of primary pores.The Linhe Formation clastic rocks have a superior preexisting material composition,with an average total content of 90%for quartz,feldspar,and rigid rock fragments,and strong resistance to compaction.The geothermal gradient in Linhe Depression in the range of(2.0–2.6)°C/100 m is low,and together with the burial history of long-term shallow burial and late rapid deep burial,it forms a weak thermal compaction diagenetic dynamic field environment.The diagenetic environment of the saline lake basin is characterized by weak fluid compaction.At the same time,the paleosalinity has zoning characteristics,and weak cementation in low salinity areas is conducive to the preservation of primary pores.The hydrodynamic conditions of sedimentation,salinity differentiation of ancient water in saline lake basins,and sand body thickness jointly control the distribution of high-quality reservoirs in the Linhe Formation.

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.

A spatiotemporal deep learning method for excavation-induced wall deflections

Data-driven approaches such as neural networks are increasingly used for deep excavations due to the growing amount of available monitoring data in practical projects.However,most neural network models only use the data from a single monitoring point and neglect the spatial relationships between multiple monitoring points.Besides,most models lack flexibility in providing predictions for multiple days after monitoring activity.This study proposes a sequence-to-sequence(seq2seq)two-dimensional(2D)convolutional long short-term memory neural network(S2SCL2D)for predicting the spatiotemporal wall deflections induced by deep excavations.The model utilizes the data from all monitoring points on the entire wall and extracts spatiotemporal features from data by combining the 2D convolutional layers and long short-term memory(LSTM)layers.The S2SCL2D model achieves a long-term prediction of wall deflections through a recursive seq2seq structure.The excavation depth,which has a significant impact on wall deflections,is also considered using a feature fusion method.An excavation project in Hangzhou,China,is used to illustrate the proposed model.The results demonstrate that the S2SCL2D model has superior prediction accuracy and robustness than that of the LSTM and S2SCL1D(one-dimensional)models.The prediction model demonstrates a strong generalizability when applied to an adjacent excavation.Based on the long-term prediction results,practitioners can plan and allocate resources in advance to address the potential engineering issues.

Deep Learning for Wireless Physical Layer: Opportunities and Challenges

Machine learning(ML) has been widely applied to the upper layers of wireless communication systems for various purposes, such as deployment of cognitive radio and communication network. However, its application to the physical layer is hampered by sophisticated channel environments and limited learning ability of conventional ML algorithms. Deep learning(DL) has been recently applied for many fields, such as computer vision and natural language processing, given its expressive capacity and convenient optimization capability. The potential application of DL to the physical layer has also been increasingly recognized because of the new features for future communications, such as complex scenarios with unknown channel models, high speed and accurate processing requirements; these features challenge conventional communication theories. This paper presents a comprehensive overview of the emerging studies on DL-based physical layer processing, including leveraging DL to redesign a module of the conventional communication system(for modulation recognition, channel decoding, and detection) and replace the communication system with a radically new architecture based on an autoencoder. These DL-based methods show promising performance improvements but have certain limitations, such as lack of solid analytical tools and use of architectures that are specifically designed for communication and implementation research, thereby motivating future research in this field.

Reduction of Electron Leakage in a Deep Ultraviolet Nitride Laser Diode with a Double-Tapered Electron Blocking Layer

A double-tapered AlGaN electron blocking layer (EBL) is proposed to apply in a deep ultraviolet semiconductor laser diode. Compared with the inverse double-tapered EBL, the laser with the double-tapered EBL shows a higher slope efficiency, which indicates that effective enhancement in the transportation of electrons and holes is achieved. Particularly, comparisons among the double-tapered EBL, the inverse double-tapered EBL, the singletapered EBL and the inverse single-tapered EBL show that the double-tapered EBL has the best performance in terms of current leakage.

Deep Learning Based Physical Layer Security of D2D Underlay Cellular Network

In order to improve the physical layer security of the device-to-device(D2D)cellular network,we propose a collaborative scheme for the transmit antenna selection and the optimal D2D pair establishment based on deep learning.Due to the mobility of users,using the current channel state information to select a transmit antenna or establish a D2D pair for the next time slot cannot ensure secure communication.Therefore,in this paper,we utilize the Echo State Network(ESN)to select the transmit antenna and the Long Short-Term Memory(LSTM)to establish the D2D pair.The simulation results show that the LSTMbased and ESN-based collaboration scheme can effectively improve the security capacity of the cellular network with D2D and increase the life of the base station.

A deep trench super-junction LDMOS with double charge compensation layer

A deep trench super-junction LDMOS with double charge compensation layer(DC DT SJ LDMOS)is proposed in this paper.Due to the capacitance effect of the deep trench which is known as silicon-insulator-silicon(SIS)capacitance,the charge balance in the super-junction region of the conventional deep trench SJ LDMOS(Con.DT SJ LDMOS)device will be broken,resulting in breakdown voltage(BV)of the device drops.DC DT SJ LDMOS solves the SIS capacitance effect by adding a vertical variable doped charge compensation layer and a triangular charge compensation layer inside the Con.DT SJ LDMOS device.Therefore,the drift region reaches an ideal charge balance state again.The electric field is optimized by double charge compensation and gate field plate so that the breakdown voltage of the proposed device is improved sharply,meanwhile the enlarged on-current region reduces its specific on-resistance.The simulation results show that compared with the Con.DT SJ LD-MOS,the BV of the DC DT SJ LDMOS has been increased from 549.5 to 705.5 V,and the R_(on,sp) decreased to 23.7 mΩ·cm^(2).

Deep and Ultra-Deep Earthquakes Worldwide, Possible Anomalies in South America

The aim of this paper is to evaluate the worldwide variation of deep and ultra-deep earthquakes (DQ and UDQ) during the period 1996-2017. This project found only three locations around the globe presenting this kind of seismicity. Although there are other global settings showing deep seismicity, they are not periodical and cannot be considered by a statistical view. The three areas with intense activity for DQ and UDQ events are located mostly in subduction areas. The largest variations of DQ and UDQ border the Pacific Ocean and include the North Pacific, South Pacific, and South America. The major difference in this set is that the first two sites are subduction zones and the South American occurrences happened in the interior of the continent. Another anomaly is an internal layer between 300 - 500 km in South America that shows no tremors in the period studied. However, below 500 km activity reappears, even at extreme depths of up to 650 km. We suggested that the reason for those occurrences would be due to an anomaly in the asthenosphere in this region. This anomaly would probably be presenting a breakable material that was pushed by the Nazca platform against the South America plate. Other depths below 100 km in all the regions are discussed as well. We suggested that the reason for those occurrences was an anomaly created in the asthenosphere as part of the process of the South America collision with the Nazca plate. Part of the Nazca plate has subducted below South America, creating a slab as deep as 500 km. The convergent slab is still moving against South America and sinking due to the gravity and rotation of the Earth. The discrepancies in the occurrences we tracked at different locations indicated that this slab had different thicknesses around South America. We found similar results for Vanuatu and Fiji;in these regions UDQ events occur at the subduction zones under the ocean with depths greater than 700 km. Here, a possible explanation is that part of the lithosphere is subducted at these depths and is causing tremors.

Toward intelligent wireless communications:Deep learning-based physical layer technologies

Advanced technologies are required in future mobile wireless networks to support services with highly diverse requirements in terms of high data rate and reliability,low latency,and massive access.Deep Learning(DL),one of the most exciting developments in machine learning and big data,has recently shown great potential in the study of wireless communications.In this article,we provide a literature review on the applications of DL in the physical layer.First,we analyze the limitations of existing signal processing techniques in terms of model accuracy,global optimality,and computational scalability.Next,we provide a brief review of classical DL frameworks.Subsequently,we discuss recent DL-based physical layer technologies,including both DL-based signal processing modules and end-to-end systems.Deep neural networks are used to replace a single or several conventional functional modules,whereas the objective of the latter is to replace the entire transceiver structure.Lastly,we discuss the open issues and research directions of the DL-based physical layer in terms of model complexity,data quality,data representation,and algorithm reliability.

Hydrocarbon test in lower-layer atmosphere to predict deep-sea petroleum or hydrate in the Okinawa Trough: an example

Light hydrocarbon (methane, ethane, propane, butane and CO2) test and C isotopic analysis of CO are conducted for over 100 lower-layer atmospheric samples from the East China Sea slope and the Okinawa Trough. The results show that the lower-layer atmosphere mainly consists of CO2 and then of CH4, and the CO2 concentrations are calculated to have a high average value of 0.87 omega/10(-2) about three times that of the regional background (0-3 omega/10(-2)). The result also shows that the average value of C isotope - 20.8 x 10(-3) is given to the CO2, inferring that it is inorganic gas. Thus, for the future 's work in the Okinawa Trough, special attention should be paid to CO2 hydrate, which is very possibly an important hydrate type.

Comparison of Persistent Deep Stable Layers over Different Valley Cities of the United States

Climate characteristics of persistent deep stable layers( PDSLs) over three valley cities of the United States( Utah's Salt Lake City,Idaho's Boise and Oregon's Medford) were compared. The results reveal that the frequency of PDSLs over Salt Lake City and Boise was very different from that of Medford; there was a good correlation between the frequency of PDSLs over Salt Lake City and MEI( Multivariate ENSO Index),while there was almost no correlation between the frequency of PDSLs over Medford and MEI; the shorter PDSL events occurred much more frequently than longer ones over the three locations; there were slight changes in the frequency and intensity of weak PDSLs with the time,while the frequency of moderate and strong PDSLs reduced obviously with the time; the appearance of PDSLs was usually accompanied by the peak of height at 500 hP a,while the disappearance of PDSLs was often accompanied by the maximum decreasing rate of temperature at 700 hP a; when PDSL events existed over the three cities,wind speed at 700 hP a and on the earth's surface was 1-2 m/s smaller than that when PDSL events disappeared,and wind direction had no obvious effect on the formation of PDSL events; over the valleys,with the increase of PDSLs,absolute humidity decreased,and relative humidity dropped greatly; within the valleys,absolute humidity also reduced,while relative humidity rose with the increase of PDSLs.

Methods and Case Studies of Ultra-deep Fault Seal Evaluation

Fault seals are significant for petroleum exploration and production.This study summarizes the fault sealing impacting factors,including lithological juxtaposition,mud smearing,fault rocks and the fault plane stress states,as well as evaluation methods like Allan maps and Shale Gouge Ratio(SGR).The seal evaluation for a wrench fault focuses on its particular structural features.The evaluation methods were applied to the Jinma-Yazihe structure and the Shunbei oilfield.The source rock is the Xujiahe Formation of the Upper Triassic,the reservoirs and caprocks being of the Shaximiao Formation of the Lower Jurassic.The fault sealing evaluations in major faults proved the reservoir formation processes in the wells Jinfo 1(JF1)and Chuanya 609(CY-609),based on the editions of the Allan map showing lithological juxtaposition,the calculation of SGR showing mud smear and analyses of fault stress states.The analyses of stress states were also applied to Shunbei 5 strike-slip fault in the Shunbei area in Tarim Basin.The various sections along the fault were of different mechanical properties,such as compression and extension.Petroleum exploration has demonstrated that the extensional sections are more favorable for oil accumulation than the compressional sections.These evolutionary methods and other understandings will help in analyses of deep fault sealing.

Spatial patterns and environmental associations of deep scattering layers in the northwestern subtropical Pacific Ocean

The mesopelagic communities are important for food web and carbon pump in ocean,but the large-scale studies of them are still limited until now because of the difficulties on sampling and analyzing of mesopelagic organisms.Mesopelagic organisms,especially micronekton,can form acoustic deep scattering layers(DSLs)and DSLs are widely observed.To explore the spatial patterns of DSLs and their possible influencing factors,the DSLs during daytime(10:00–14:00)were investigated in the subtropical northwestern Pacific Ocean(13°–23.5°N,153°–163°E)using a shipboard acoustic Doppler current profiler at 38 kHz.The study area was divided into three parts using k-means cluster analysis:the northern part(NP,22°–24°N),the central part(CP,17°–22°N),and the southern part(SP,12°–17°N).The characteristics of DSLs varied widely with latitudinal gradient.Deepest core DSLs(523.5 m±17.4 m),largest nautical area scattering coefficient(NASC)(130.8 m^(2)/n mile^(2)±41.0 m^(2)/n mile^(2)),and most concentrated DSLs(mesopelagic organisms gathering level,6.7%±0.7%)were observed in NP.The proportion of migration was also stronger in NP(39.7%)than those in other parts(18.6%in CP and 21.5%in SP)for mesopelagic organisms.The latitudinal variation of DSLs was probably caused by changes in oxygen concentration and light intensity of mesopelagic zones.A positive relationship between NASC and primary productivity was identified.A four-months lag was seemed to exist.This study provides the first basin-scale baselines information of mesopelagic communities in the northwest Pacific with acoustic approach.Further researches are suggested to gain understandings of seasonal and annual variations of DSLs in the region.

Research on Thin Layer Structure Identification and Sedimentary Facies of Middle and Deep Layers Based on Reflection Coefficient Inversion—By Taking Dongying Formation of CFD Oilfield in Bohai Offshore as an Example

The sand layer B of Dongying Formation of CFD oilfield in Bohai offshore belongs to the middle deep layer of buried hill overlap deposit. Its reservoir distribution has the characteristics of large burial depth, thin thickness and rapidly lateral change. Because of low resolution of seismic data and overlying sand layer. It is difficult to identify and interpret the structure of sand layer accurately. The uncertainty of structure and reservoir restricts the fine development of B sand layer. In order to identify the top surface of reservoir effectively. The seismic data are processed by using the reflection coefficient inversion method. The results show that the inversion resolution of reflection coefficient is significantly higher than that of original data. The top surface of sand layer B and its overlying sand layer can be well identified and traced. Carrying out structural interpretation of B sand layer based on reflection coefficient inversion data and the microstructure and the formation tip extinction point are implemented. Based on the constraint of new interpretation level, the sedimentary facies plane distribution of B sand layer is described and make prediction of dominant reservoir development area in detail combining with sedimentary paleogeomorphology, along layer attribute section and limited drilling data. The research shows that the study area is mainly from the northwest material sources, the slope belt in the northwest is close to the lake shoreline with a gentle slope and shallow water depositional environment, which is located on the main transport and deposition channels. The shallow water gentle slope landform is suitable for forming large-area sand bar deposition, mainly composed of underwater distributary channel and debouch bars facies, which is the dominant reservoir development area. The research conclusion guides the deployment and implementation of the development well location effectively.

Geological characteristics and models of fault-foldfracture body in deep tight sandstone of the second member of Upper Triassic Xujiahe Formation in Xinchang structural belt of Sichuan Basin,SW China

In the second member of the Upper Triassic Xujiahe Formation(T_(3)x_(2))in the Xinchang area,western Sichuan Basin,only a low percent of reserves has been recovered,and the geological model of gas reservoir sweet spot remains unclear.Based on a large number of core,field outcrop,test and logging-seismic data,the T_(3)x_(2) gas reservoir in the Xinchang area is examined.The concept of fault-fold-fracture body(FFFB)is proposed,and its types are recognized.The main factors controlling fracture development are identified,and the geological models of FFFB are established.FFFB refers to faults,folds and associated fractures reservoirs.According to the characteristics and genesis,FFFBs can be divided into three types:fault-fracture body,fold-fracture body,and fault-fold body.In the hanging wall of the fault,the closer to the fault,the more developed the effective fractures;the greater the fold amplitude and the closer to the fold hinge plane,the more developed the effective fractures.Two types of geological models of FFFB are established:fault-fold fracture,and matrix storage and permeability.The former can be divided into two subtypes:network fracture,and single structural fracture,and the later can be divided into three subtypes:bedding fracture,low permeability pore,and extremely low permeability pore.The process for evaluating favorable FFFB zones was formed to define favorable development targets and support the well deployment for purpose of high production.The study results provide a reference for the exploration and development of deep tight sandstone oil and gas reservoirs in China.

The Project “Development and Commercial Application of Technology for Ultra-deep HDS of Diesel(RTS)” Passed SINOPEC's Appraisal

On December 9, 2014 the scientific research project ＂Developmentand commercial application of technology forultra-deep HDS of diesel （RTS）＂ jointly performed bythe SINOPEC Research Institute of Petroleum Processing（RIPP）, the Yanshan Petrochemical Branch Company（YPBC）, the Maoming Petrochemical Branch Companyand the Guangzhou Petrochemical Branch Company haspassed in Beijing the technical appraisal organized by theScience and Technology Division of the Sinopec Corp.

Deepwater and Deep Layers： the Strategic Choices for China＇s Oil and Gas Resources Development

On December 2,2017,the 2^(nd) Seminaron the Development Strategy of China‘s Oil and Gas Resources was held in Beijing.This seminar was hosted by the Strategic Research Center for Oil and Gas Resources,Ministry of Land(MLR)and Resources and sponsored by both the CNOOC Exploration Department and the School of Ocean and Earth Sciences of Tongji University.With the theme of"deepwater and deep-layer oil and gas resources and development strategy",the

Variation in concentration of dissolved silicate in the Eastern Philippine deep sea

Upper Circumpolar Deep Water(UCDW)and North Pacifi c Deep Water(NPDW)coexist in the upper deep layer(i.e.,with a 1.2-2.0-℃potential temperature range and a 2000-4100-dbar pressure range)of the Eastern Philippine Sea.They have similar properties in potential temperature and salinity,while have a signifi cant diff erence in dissolved silicate.Based on the repeated observations along a 137°E transect from the World Ocean Database(WOD18),this study revealed the interannual variability of dissolved silicate in the upper deep layer of the Eastern Philippine Sea.Dissolved silicate increased in 1995,1996,2005,2006,and 2007,and decreased in 1997,2000,2001,2002,and 2004.Composition analysis showed that the large diff erence between positive and negative dissolved silicate anomalies occurred mainly at~15°N and north of 25°N,with the concentration reaching 4.25μmol/g.Further analysis indicated that the interannual dissolved silicate variability was related to the zonal current variation in the upper deep layer.The relatively strong(weak)westward current transport increased(decreased)NPDW to the Eastern Philippine Sea,thereby resulting in increased(decreased)dissolved silicate.

Hybrid Deep Learning-Improved BAT Optimization Algorithm for Soil Classification Using Hyperspectral Features

Now a days,Remote Sensing(RS)techniques are used for earth observation and for detection of soil types with high accuracy and better reliability.This technique provides perspective view of spatial resolution and aids in instantaneous measurement of soil’s minerals and its characteristics.There are a few challenges that is present in soil classification using image enhancement such as,locating and plotting soil boundaries,slopes,hazardous areas,drainage condition,land use,vegetation etc.There are some traditional approaches which involves few drawbacks such as,manual involvement which results in inaccuracy due to human interference,time consuming,inconsistent prediction etc.To overcome these draw backs and to improve the predictive analysis of soil characteristics,we propose a Hybrid Deep Learning improved BAT optimization algorithm(HDIB)for soil classification using remote sensing hyperspectral features.In HDIB,we propose a spontaneous BAT optimization algorithm for feature extraction of both spectral-spatial features by choosing pure pixels from the Hyper Spectral(HS)image.Spectral-spatial vector as training illustrations is attained by merging spatial and spectral vector by means of priority stacking methodology.Then,a recurring Deep Learning(DL)Neural Network(NN)is used for classifying the HS images,considering the datasets of Pavia University,Salinas and Tamil Nadu Hill Scene,which in turn improves the reliability of classification.Finally,the performance of the proposed HDIB based soil classifier is compared and analyzed with existing methodologies like Single Layer Perceptron(SLP),Convolutional Neural Networks(CNN)and Deep Metric Learning(DML)and it shows an improved classification accuracy of 99.87%,98.34%and 99.9%for Tamil Nadu Hills dataset,Pavia University and Salinas scene datasets respectively.