In dipole acoustic logging,the dispersion of flexural waves complicates shear wave velocity extraction.Traditional dispersion correction algorithms rely on repeated forward modeling of the dispersion equation,which is extremely time-consuming.To address this issue,this paper proposes a dispersion correction method for dipole flexural waves based on a fully connected deep neural network.First,a large-scale dataset is generated by forward modeling using the dispersion equation,and a multi-layer fully connected deep neural network is constructed to learn the nonlinear mapping between formation parameters and dispersion curves,enabling fast forward simulation of dispersion curves.Second,a one-dimensional density clustering algorithm based on neighborhood consistency is introduced to automatically extract effective dispersion features from measured data.By combining this with the fully connected deep neural network bending flexural wave dispersion forward modeling method and the least-squares matching algorithm,an efficient workflow for dipole flexural wave dispersion correction is established to accurately extract shear wave velocity.The research results show that:①The fully connected deep neural network can generate one thousand predicted dispersion curves in only 0.277 s,achieving more than three orders of magnitude speedup compared to the dispersion equation(320.9 s)while maintaining a prediction error below 0.5%,thereby enabling real-time forward modeling at the millisecond level.②The density clustering algorithm effectively removes abrupt noise and identifies valid dispersion features,providing high signal-to-noise ratio inputs data for the correction workflow.③Application of the dipole flexural wave dispersion correction method to an actual well in the eastern South China Sea demonstrates that the corrected shear wave velocity is significantly more accurate than the original extraction result.Using this shear wave velocity for pay zone identification,the P-wave to S-wave velocity ratio and Lamé impedance sections accurately delineate sandstone reservoirs and gas-bearing zones,substantially enhancing the accuracy of seismic inversion and fluid identification.It is concluded that this method not only provides a new tool for accurate shear wave velocity extraction in dipole acoustic logging,but also serves as a valuable reference for constructing intelligent well-log data processing workflows.
Coalbed methane serves as a vital component of clean energy,and its efficient development encounters technical bottlenecks in the dynamic monitoring of production profiles.To tackle the difficulties in monitoring the production profiles during commingled drainage of low-pressure,thin-interbedded coalbed methane wells,as well as the insufficient accuracy of conventional methods and restricted instrument running,this paper takes well A1 in the Bijie pilot area of Guizhou province as the research object and proposes an evaluation method for coalbed methane well production profiles based on distributed optical fiber sensing.The study adopts a fixed-point optical fiber binding process outside the tubing to realize synchronous acquisition of full-wellbore temperature and acoustic signals under multiple working systems in well A1.A production profile interpretation framework is established based on the Ramey wellbore heat exchange model,Joule-Thomson effect and two-phase flow drift model.A coupled interpretation strategy is put forward,which uses the steady-state frequency band energy(FBE)analysis of distributed acoustic sensing(DAS)to constrain the temperature field inversion of distributed temperature sensing(DTS).Specifically,the steady-state frequency band energy of DAS in characteristic frequency bands is used to extract the water production rate information of each layer,so as to constrain the gas-liquid ratio parameters in the DTS temperature forward model.This strategy solves the strong multi-solution problem of traditional single DTS interpretation under gas-liquid two-phase flow conditions and realizes the fine quantitative allocation of gas and water production rates of each coal seam.Monitoring data clearly indicate the main gas-producing and water-producing layers,and the optical fiber interpretation results show good consistency with open-hole logging conclusions and fracturing operation parameters,which verifies the reliability of optical fiber monitoring in coalbed methane wells.This study provides a reliable dynamic monitoring method for the development of low-pressure,thin-interbedded coalbed methane and offers scientific guidance for the optimization and adjustment of production technologies in the process of coalbed methane development.
With the transition of well log evaluation towards data-driven paradigms, high-quality data has become the cornerstone for constructing high-confidence geological models. Consequently, data cleaning is a critical pre-processing step to ensure algorithm robustness and interpretation accuracy. Through a comprehensive literature review, this study traces the evolution of cleaning technologies since the 1970s, establishing a standardized framework from quality identification to performance evaluation. We analyze the formation mechanisms of noise, outliers, and missing values, identifying instrument acquisition, borehole environment, and operational factors as dominant controls. Synthesizing this development, we propose a five-stage evolutionary model comprising numerical analysis, signal processing, traditional machine learning, deep learning, and large language model (LLM). Based on this progression, we construct a technical classification system covering numerical analysis, signal processing, artificial intelligence, and multi-method fusion, comparing typical methods in terms of algorithmic principles and practical efficacy. Finally, forecasts future trends, highlighting intelligence, adaptive learning, real-time processing, cloud-edge collaboration, and knowledge-driven approaches. This research offers a theoretical foundation and practical reference for the iterative upgrading and standardization of well logging data processing technologies.
To address the problems of insufficient sensitivity analysis on the throttling pressure drop of gas nozzles,great difficulties in downhole gas nozzle selection and layered flow rate allocation in layered gas injection wells with CO2-bearing natural gas,and to meet the engineering demands of complex deep-sea well conditions,this paper takes the layered gas injection gas nozzles for CO2-bearing natural gas as the research object.Based on the Fluent numerical simulation platform,a numerical calculation system including the energy equation,mass equation,momentum equation,multi-component transport model and standard k-ε turbulence model is established.Using the control variable method,this paper investigates the effects of CO2 molar ratio,gas nozzle diameter,flow rate,temperature,outlet pressure and multi-factor coupling on the throttling pressure drop of gas nozzles,reveals the pressure distribution law inside the gas nozzle,and conducts field tests on an oil production platform in the South China Sea.The results show that:①The throttling pressure drop of the gas nozzle is mainly concentrated in the contraction zone of the nozzle.When the CO2 molar ratio is 20%～80%,the throttling pressure drop decreases in a quadratic function with the increase of CO2 content,and the pressure drop changes more significantly when the CO2 molar ratio is 20%～50%.②Under the action of a single factor,the throttling pressure drop increases in a quadratic function with the increase of flow rate,increases linearly with the rise of temperature,and decreases gradually with the increase of gas nozzle diameter and outlet pressure.③Under the flow rate-temperature coupling effect,the flow rate dominates the increase of pressure drop,and the decrease of temperature can slightly offset the increase of pressure drop.Under the outlet pressure-temperature coupling effect,the two factors synergistically enhance the inhibition effect on pressure drop.④In the field test,a gas nozzle with a diameter of 5.2 mm is adopted,the actual wellhead gas injection pressure is 0.6 MPa higher than the target pressure,and the error between the actual injection flow rate(7.2×104 m3/d)and the target injection flow rate(6.1×104 m3/d)is less than 20%,which meets the requirements of field layered gas injection allocation.The research on the variation law of the throttling pressure drop of CO2-bearing natural gas nozzles under multi-factor and coupling effects can provide reliable theoretical and technical support for the accurate selection of downhole nozzles and the dynamic regulation of layered gas injection flow rate in layered gas injection wells with high CO2 content.
To address the issue that casing scale interferes with the calculation of formation macroscopic capture cross-section(Sigma)and reduces the accuracy of residual oil saturation evaluation in pulsed neutron-neutron(PNN)logging through cased holes,this paper establishes a three-dimensional numerical model of PNN instrument,wellbore and formation using the Monte Carlo simulation software.The simulation reproduces the slowing-down and capture processes of neutrons in the scaled borehole and formation.The effects of scale type,thickness,and composition on thermal neutron time spectra,neutron distribution,and measured Sigma are analyzed.A self-compensation correction method for the formation Sigma is developed and validated with field log data.The results show that:①Casing scale replaces borehole fluid,reducing the fast neutron slowing-down capability.Corrosion scale containing elements with high thermal neutron capture cross-section(e.g.,Cl)shortens the thermal neutron lifetime,leading to overestimation of the formation Sigma,whereas sedimentary scale weakens the thermal neutron capture capability,resulting in underestimation of Sigma.②The formation Sigma and the near-to-far detector thermal neutron count ratio increase linearly with the thickness of corrosion scale,while they decrease linearly with the thickness of sedimentary scale.The magnitude of this effect varies with formation porosity.For each 1 cm increase in corrosion scale thickness,formation Sigma increases by 3.37 c.u.in a formation with 8%porosity and by 3.69 c.u.in a formation with 15%porosity.③A higher NaCl content in the corrosion scale leads to a more pronounced influence on the formation Sigma.④The correction amount of the formation Sigma exhibits a linear relationship with the difference between the cased-hole neutron porosity and the compensated neutron porosity.The proposed self-compensation correction method achieves an average absolute error of only 0.29 c.u.after correction.It is concluded that casing scale significantly disturbs the logging response by altering neutron slowing-down and capture processes.The self-compensation correction method based on the thermal neutron count ratio and compensated neutron porosity effectively eliminates the scale effect,substantially improving the accuracy of formation Sigma calculation and the reliability of residual oil saturation evaluation,thus providing key technical support for dynamic monitoring of residual oil in cased holes.
To improve the automation,objectivity,and engineering efficiency of sedimentary unit boundary correlation in the Sanan development area of the Daqing oilfield,where dense well patterns and highly subdivided stratigraphic sequences make manual correlation inefficient,subjective,and inconsistent in complex intervals,an intelligent correlation method integrating multimodal features and deep reinforcement learning is developed.Using SP,GR,RLLD,and AC logging curves together with their roughness attributes,this study investigated collaborative representation of multimodal logging information,optimization of inter-well boundary matching paths,and automatic identification mechanisms for complex intervals.First,multi-source logging curves are synchronously sampled,standardized,smoothed,and organized into sliding-window inputs.Then,convolutional feature extraction,bidirectional long short-term memory(BiLSTM)temporal modeling,and attention-based adaptive fusion are employed to obtain integrated features for boundary identification.Furthermore,curve-roughness similarity measurement,a variable-window search strategy,and deep Q-network(DQN)-based sequential decision-making are combined to achieve automatic boundary matching and global path optimization between reference wells and target wells.The results show that,based on measured data from 1 500 wells in 23 blocks,the proposed method achieved an overall matching rate of 94.1%on 1 350 test wells,with an average processing time of approximately 52 s per well.Among 105 000 sedimentary unit boundary samples,78.1%of the absolute boundary-depth errors are controlled within 0.3 m,89.5%within 0.5 m,and 96.2%within 0.7 m.The comprehensive identification accuracy for six major marker beds is about 94.3%;among them,four marker beds achieved accuracies higher than 95%,while the remaining two reached about 91%～93%.Compared with the traditional correlation coefficient plus gradient method,as well as convolutional neural network(CNN)-BiLSTM and graph neural network(GNN)models,the proposed method outperforms comparative methods across all metrics including stratified identification accuracy,F1 score,intersection over union(IoU),overall matching rate,and processing efficiency.It is concluded that this method can effectively improve the accuracy,stability,and engineering applicability of sedimentary unit boundary correlation under large-scale well-network conditions,and can provide reliable technical support for fine stratigraphic correlation,reservoir characterization,and subsequent geological modeling.
The precise diagnosis of the fracturing effect of shale oil horizontal wells is the key to optimizing the process and increasing production capacity.In response to the limitations of traditional microseismic monitoring,such as insufficient resolution,and the tracer method being time-consuming and unable to provide real-time feedback,this study focuses on the shale oil horizontal wells in well block M and constructs a detailed diagnostic system centered on the dynamic monitoring of high-frequency pressure fields.This method is based on the transient signals of pump-stop water hammer and pressure attenuation.It deeply analyzes the application mechanisms of cepstral analysis inversion for fracture geometry,the dual-logarithmic curve of pressure derivative for discriminating the flow stage,and the negative pressure derivative for diagnosing engineering integrity.A three-dimensional diagnostic process of"structure—capacity—integrity"is established,followed by multi-well section identification and cross-validation.The research results show that:①Based on cepstral analysis,the number of fractures and the location of liquid injection points have been accurately inverted.Among the 27 fractured sections,24 sections exhibited three or more fractures,corresponding to a compliance rate of 88.9%.②The flow capacity of fractures can be clearly characterized by classifying four flow regimes:high conductivity,finite-conductivity,infinite-conductivity,and boundary-dominated flow,using log-log plots of pressure derivative.③The negative pressure derivative has been used to diagnose the sealing performance of the bridge plug in real time.The distribution characteristics of continuous sealing failure in sections 4～6 of well X1 and discrete sealing failure in the middle part of well X101 have been identified.④The comprehensive diagnosis identifies four typical fracture network morphologies:multi-level cross-fractures,thick and long straight fractures,fine and short fractures,and small-scale fracture networks.The diagnostic results have been cross-validated with logging and tracer data,achieving a comprehensive compliance rate of 85.2%.It is concluded that the high-frequency pressure field dynamic monitoring technology can achieve high-precision and real-time diagnosis of fractures and engineering anomalies,forming a complete technical chain from data acquisition,multi-dimensional diagnosis to cross-validation.This technology provides reliable technical support for the optimization of shale oil fracturing processes and the increase of single-well production capacity.
Nuclear magnetic resonance(NMR)logging can effectively evaluate petrophysical parameters of reservoirs,providing an important basis for oil and gas resource exploration.It exhibits distinct advantages in the identification and quantitative evaluation of fluids.However,unconventional reservoirs are characterized by low porosity,resulting in weak fluid signals detected by NMR logging,low signal-to-noise ratio of echo data,and high uncertainty in the inverted T2 spectra,which in turn affects the reliability of NMR logging formation evaluation.To address these issues,this paper proposes an SGMD-Hurst method combining symplectic geometry mode decomposition(SGMD)and the Hurst index for noise reduction of low signal-to-noise ratio NMR echo data.In this method,the echo data are first decomposed into multiple symplectic geometry components(SGC)using SGMD.Secondly,the Hurst index is calculated for each SGC,and effective SGCs with long-range correlation are screened out based on the Hurst index.Finally,the selected effective SGCs are reconstructed to obtain denoised echo data that retain the characteristics of the original signal while effectively suppressing noise.On this basis,the denoising performance and adaptability of the SGMD-Hurst method are analyzed using numerical simulations and lowsignal-to-noise ratio NMR logging data respectively.The results show that:①Compared with the T2 spectra inverted from the original echo data,as well as data denoised by empirical mode decomposition and the traditional SGMD method,the T2 spectra inverted from the SGMD-Hurst denoised echo data have clearer peak shapes and can more accurately distinguish the positions between the irreducible water peak and the movable fluid peak.②In numerical simulations,the porosity inverted and calculated from the echo data denoised by the SGMD-Hurst is closer to the true value of the model,with a lower root-mean-square error.③In the processing of NMR logging data,the SGMD-Hurst method can still restore the main distribution interval of the T2 spectrum under low-SNR conditions,and the calculated porosity is more consistent with core analysis data than that obtained by traditional denoising methods.It is concluded that the SGMD-Hurst method effectively improves the quality of low signal-to-noise ratio NMR echo data and could provides reliable data preprocessing technical support for the fine evaluation of low-porosity and low-permeability oil and gas reservoirs.
To enhance the reliability of reservoir evaluation in complex wells and to address the issues of inconsistent responses and cross-validation difficulties arising from differences in measurement principles and environmental conditions between logging while drilling(LWD)resistivity and induction logging.An analytical framework for response differences based on measurement principles and various formation conditions is established based on LWD and induction logging.The sensitivity characteristics of high-frequency LWD electromagnetic resistivity measurements to thin beds and boundaries,as well as the cause of the horn-shaped anomalies are revealed.The influences of bed thickness,dipping angle,and resistivity contrast on the two types of logging responses are analyzed.On this basis,a comparison is made between inversion using a single resistivity data source and joint inversion.A comprehensive quality control method for LWD resistivity data,termed"calibration—correction—validation",is proposed and standardized,with quantitative indicators and operational specifications for air calibration,full-temperature calibration,and water tank calibration clearly defined.The results show that:①Differences in responses between LWD resistivity and induction logging stem from variations in measurement timing,operating frequency and detection characteristics.These are governed by the interplay of bed thickness,dipping angle and resistivity contrast,and are particularly pronounced in formations characterized by thin beds,high dipping angle and high resistivity contrast.②High-frequency LWD electromagnetic resistivity measurements are more sensitive to thin beds and boundaries and prone to horn-shaped anomalies,whereas low-frequency induction measurements offer deeper detection but yield weak signals in high-resistivity layers.③LWD electromagnetic resistivity and induction logging data joint inversion effectively reduces inversion non-uniqueness,achieving faster convergence and higher accuracy than inversion using a single data source.④LWD electromagnetic resistivity data subjected to the three-level calibration quality control show consistent macroscopic trends with induction logging data and hold unique value in original formation characterization,while wireline data are more suitable for detailed characterization of reservoir profiles.It is concluded that LWD electromagnetic resistivity and induction logging data have complementary advantages.A single rigid evaluation criterion should be discarded,and multi-source collaborative evaluation should be implemented based on the characteristics of electrical differences.Through differentiated application and joint inversion,the capability of high-precision formation evaluation for complex wells can be enhanced.The proposed quality control method provides reliable technical support for standardized processing of LWD electromagnetic resistivity data.
To overcome the limitations of the current electrical method in measuring the fluid holdup in horizontal wells,such as the single measurement parameter and the complex structure of the measurement device,a new method for measuring the fluid holdup in horizontal wells based on the complex impedance method is proposed,and its feasibility is systematically studied.The complex impedance spectrum method is adopted as the measurement principle,and a 2N sequence pseudo-random signal is used as the multi-frequency synchronous excitation source.Through a combination of theoretical analysis and numerical simulation,the theoretical basis of the complex impedance method for holdup measurement is deeply explored.The COMSOL finite element simulation software is used to conduct forward research and obtain the complex impedance spectrum response characteristics of oil-water two-phase flow stratified flow under different holdup conditions.The real part ratio and the imaginary part slope ratio of the complex impedance are extracted from the spectrum data as key dimensionless characteristic parameters reflecting the water holdup.The radial basis function(RBF)neural network is introduced to establish a nonlinear inversion fitting model between the characteristic parameters and the water holdup.A preliminary design of the fluid holdup measurement system based on the complex impedance method is carried out,and indoor simulation experiments of oil-water two-phase flow stratified flow are conducted to verify the actual measurement feasibility of this method.The research results show that:①The complex impedance spectrum of oil-water two-phase flow can effectively reflect its holdup information,and the extracted real part ratio and imaginary part slope ratio characteristic parameters have a significant nonlinear mapping relationship with the water holdup,which can effectively eliminate the influence of different salinity water on the measurement results.②Compared with the polynomial fitting method,the RBF neural network shows better nonlinear approximation ability in the inversion of water holdup,with the sum of squared errors reduced by 93.7%,the coefficient of determination R2 reaching 0.999 9,and the root mean square error being only 0.113 2,significantly improving the prediction accuracy.③Under the indoor simulation experiment conditions,the recognition results of the designed measurement system for oil-water two-phase flow with different water holdups are basically consistent with the actual water holdup,and the average coincidence rate of oil-water identification in stratified flow is greater than 90%,with the maximum relative error being 12%.The conclusion is that the complex impedance method for measuring the fluid holdup in horizontal wells is feasible in theory.The preliminary designed measurement system based on the complex impedance method can effectively identify the water holdup in the fluid,verifying the practical application potential of the complex impedance method in the field of holdup measurement in horizontal wells,and laying a theoretical foundation for the subsequent development of complex impedance holdup measurement technology under multi-flow patterns and complex working conditions.
In order to ensure that the oil logging instrument shell can effectively protect the internal instrument under the condition of high temperature and high pressure in the downhole,and meet the requirements of safe use,this paper takes a single type of oil logging instrument shell as the research object,and carries out systematic test and simulation analysis.First,a comprehensive material property test is carried out on each component of the instrument shell by combining experimental test with finite element simulation.The key mechanical properties of the material under simulated downhole high temperature and high pressure environment are systematically tested,and the mechanical response characteristics and variation rules of the material under extreme working conditions are clarified.On this basis,combined with the actual working conditions of the downhole,a finite element model for the strength analysis of the casing of the logging tool is established.Through the simulation analysis system,the equivalent stress distribution characteristics of the shell of a single logging tool under the downhole high temperature and high pressure conditions are explored,and the potential weak parts of the shell are accurately located.The results show that the weak parts of the shell are concentrated in the structural geometric mutation area,where the equivalent stress level is high and there are structural safety hazards.Aiming at the weak parts,the structural local optimization design scheme is adopted to improve the structure.After optimization,the equivalent stress of the weak parts is significantly reduced to the allowable stress range of the material,the overall stress distribution uniformity of the shell is obviously improved,and the bearing capacity of the structure is effectively enhanced.It is concluded that the method of combining material test and finite element simulation adopted in this paper is feasible and effective.The obtained material performance parameters,stress distribution law and optimization scheme provide reliable theoretical basis and technical support for the structural design,strength check and optimization improvement of the shell of similar logging tools.
To address the current challenges in predicting in-situ stress in ultra-deep basement gas reservoirs,such as the scarcity of measured data and unclear adaptability of log interpretation methods,and to support the efficient exploration and development of the first ultra-deep basement gas reservoir in the Qaidam basin(the K2 block basement gas reservoir),this paper takes K2 block as the research object.Drilling,array acoustic logging,conventional logging,and hydraulic fracturing data are employed to conduct research on the logging evaluation method for in-situ stress in ultra-deep basement gas reservoirs.By introducing the correction coefficient C* and the non-equilibrium structural factor Ub,the Newberry model is improved,and a log interpretation model for in-situ stress applicable to ultra-deep basement rocks is established.The results show that:①The transformation relationship between compressional and shear wave travel time in bedrock,established based on array acoustic logging data,provides a reliable basis for predicting shear wave travel time.Subsequently,elastic modulus and Poisson's ratio parameter profiles are obtained using physical equations.These parameters exhibit significant vertical variations,indicating strong heterogeneity in the mechanical properties of the basement.②Measured in-situ stress data for eight well intervals in the basement are obtained using the hydraulic fracturing method,clarifying that the in-situ stress state in the ultra-deep basement satisfies vertical principal stress(σv)>maximum horizontal principal stress(σH)>minimum horizontal principal stress(σh),with the maximum horizontal principal stress oriented in the NWW direction.③Measured hydraulic fracturing data confirm that the horizontal stress difference(σH-σh)in the ultra-deep basement decreases slightly with increasing depth,mainly ranging from 8 MPa to 12 MPa,which is favorable for forming complex fracture networks during fracturing.④The improved Newberry model can effectively predict basement in-situ stress,and the interpretation results show good consistency with the measured hydraulic fracturing data.It is concluded that the in-situ stress log interpretation method established in this paper has high prediction accuracy and is suitable for ultra-deep basement rocks.The research findings provide a reliable technical means for in-situ stress evaluation in ultra-deep basement gas reservoirs and offer significant reference value for the efficient exploration and development of similar reservoirs.
To achieve high-precision real-time detection of downhole fish and ensure safe and efficient operation of oil and gas fields,a field-programmable gate array(FPGA)based downhole ultrasonic forward-looking detection system is developed to overcome the bottlenecks of existing ultrasonic detection systems,including wired dependence,insufficient accuracy in echo first-arrival extraction under low signal-to-noise ratio,and poor high-temperature adaptability.The system integrates ultrasonic transmission,echo conditioning,and data acquisition circuits.For downhole environments with low signal-to-noise ratio,limited resources,and high temperature,a hardware acceleration architecture of the Akaike information criterion(AIC)algorithm for edge deployment is proposed.The search range is narrowed by fixed time window constraint,high-cost logarithmic operations are replaced by coordinate rotation digital computer(CORDIC)iteration units,and a fully pipelined parallel variance calculation path is constructed to realize lightweight algorithm and hardware co-optimization.Results show that:①The system clearly images the fish-top shape,and the dimensional reconstruction error of fish is 3%at room temperature and low SNR,which is 1.33 times more accurate than the threshold method.②The single-point ranging function remains stable at 120℃,with a ranging error less than 2.5 mm.③The optimized AIC hardware architecture consumes only 9 850 LUTs and 116 DSP resources,and the processing delay for 3 001 points is 6.8 ms,which is significantly superior to the traditional look-up table method and square approximation in resource utilization and processing efficiency.④The FPGA logic resource occupation is reasonable,with LUT consumption accounting for 39%of the total resources and DSP consumption accounting for 78%,which meets the downhole real-time processing requirements.It is concluded that the system realizes cableless deployment and real-time imaging,satisfies downhole detection requirements in terms of accuracy,real-time performance,and high-temperature stability.The FPGA-accelerated AIC algorithm effectively improves the accuracy of echo first-arrival extraction under low SNR and provides a reliable technical solution for deep-well fish detection.
Throughout the full lifecycle of underground gas storage—including construction,capacity expansion,and cyclic injection and production—the dynamic evaluation of the gas-liquid interface remains a key research focus and technical challenge.Accurately quantifying the temporal evolution of gas saturation is of significant importance across different operational stages of gas storage development.During the early construction phase,thermal neutron imaging logging often suffers from insufficient interpretation accuracy and poor computational sensitivity under conditions of low gas saturation and low formation water salinity.To address these issues,this study leverages the response mechanism of thermal neutron imaging logging and selects three key sensitive parameters:long-space count rate,short-space count rate,and formation thermal neutron capture cross-section.The entropy weight method is employed to objectively determine the contribution weights of these parameters to gas saturation.A gas-liquid saturation sensitivity factor is thereby constructed,and a quantitative gas saturation calculation model tailored for the early-stage development of gas storage reservoirs is established.The proposed method effectively suppresses interference caused by low formation water salinity and fluctuations in neutron tube yield,significantly enhancing the detection sensitivity to weak gas-phase signals and improving the precision of gas-water interface delineation.Applied to 18 wells'operation during the initial construction phase of the M oilfield-based gas storage,the approach enabled the establishment of a quantitative evaluation standard and interpretation chart for monitoring wells.Thresholds for classifying six reservoir types—including gas zones and gas-water transition zones—are clearly defined,leading to the cumulative identification of seven effective gas-bearing layers with a total thickness of 21.8 meters.Single-well validation demonstrates that the interpretation results from the new method show excellent agreement with temporary gas production test rates and gas inflow profiling,achieving a correlation coefficient of 0.93.This study confirms that the method exhibits strong adaptability and robust anti-interference capability.It effectively solves the problem of low sensitivity in saturation evaluation caused by low gas content and severe water-phase interference in the initial stage of gas storage construction,provides reliable technical support for gas-liquid dynamic monitoring in the initial construction stage of water-flooded gas storage.
Aiming at the insufficient accuracy of existing oil and gas well visual detection technology in the quantitative analysis of perforation parameters,an imaging vision model of downhole camera is constructed.The perspective transformation and cylindrical unfolding algorithm are combined to realize the mapping from annular wellbore images to two-dimensional plane images.A local adaptive threshold algorithm is adopted to achieve accurate segmentation of perforation regions.The traditional Canny edge detection algorithm is improved to enhance the sensitivity of gradient direction and suppress edge fracture and false edge interference.Morphological parameters such as perforation area,perimeter and roundness are extracted for quantitative analysis.The research results show that:①The proposed method can effectively eliminate the geometric distortion of curved surface imaging and realize high-precision unfolding of wellbore images.②Local adaptive threshold segmentation can dynamically adapt to the uneven downhole illumination scenario,achieving prominent separation effect between perforations and background.③The improved Canny algorithm presents high integrity of edge extraction,with false edges and edge fractures significantly suppressed.④The average error for standard perforation area detection with hole diameters of 8 mm and 10 mm was only 13.19 mm,a 54.72%reduction in error compared to traditional methods.⑤Multi-dimensional parameters including aperture,area and roundness can be output synchronously to accurately identify the erosion and deformation degree of perforations.It is concluded that this method can improves the detection accuracy of perforation parameters and realizes quantitative diagnosis of perforation damage,which provides a technical support for perforation condition evaluation and reservoir stimulation optimization of oil and gas wells.
To enhance the readability and feature distinguishability of time—frequency diagrams of logging while drilling(LWD)telemetry signals in complex noise environments,this paper takes the drilling fluid pulse telemetry system as the research object and proposes an adaptive amplitude mapping method based on statistical frequency histograms.This method counts the amplitude values of each frequency band after time—frequency transformation to generate a frequency distribution histogram,uses a Sigmoid kernel function to perform nonlinear mapping with limited ranges on the statistical values of each histogram bin,and then achieves amplitude normalization through linear remapping,thereby effectively highlights the energy difference between signal and noise and prevents weak energy features from being suppressed.This method overcomes the problem of weak signal feature loss in conventional normalization by adaptively adjusting the mapping interval through frequency-band statistical characteristics,enhancing the visual presentation of weak signals.Meanwhile,a YOLO(You Only Look Once)deep learning model is introduced to detect in real time the signal band and three typical types of noise including pump noise,mechanical noise,and drilling fluid noise in time—frequency diagrams,so as to enhance the visual expressiveness.The research results show that:the Renyi entropy of the proposed method is 10.27,which is about 3.20 lower than that of the traditional method,indicating more concentrated energy distribution and more prominent features;in terms of subjective scoring,both feature resolution and interference distinguishability are superior to those of the traditional method,with an overall score of 8.8,approximately 2.3 points higher than the traditional method;the YOLO model achieves detection accuracies over 98%for the signal band,drilling fluid noise,and mechanical noise,over 81%for pump noise,and an overall mean average precision mAP50(mean Average Precision at IoU threshold 0.5)for the four categories exceeding 94%,fully validating the effectiveness of the proposed visualization method in feature representation.It is concluded that this method can significantly improve the feature representation capability of time—frequency diagrams of LWD signals,providing clear and reliable visual support for downhole data processing and fault diagnosis.
To address the challenge of distinguishing between gas and water zones in the heterogeneous,low-porosity sandstone condensate reservoirs of the Qulitag structural belt in the Kuqa piedmont region,where resistivity differences are minimal and difficult to accurately identify,we integrated rock physics data from grain size analysis,whole-rock X-ray diffraction,clay mineral X-ray diffraction,nuclear magnetic resonance(NMR),and mercury intrusion experiments with well logging curves to analyze the four-property relationships of reservoirs and the genesis of low resistivity contrast condensate gas reservoirs.The Cretaceous Bashijiqike formation in the Qulitag structural belt is predominantly composed of fine-grained sandstones.Resistivity in the reservoir interval shows no significant correlation with grain size,indicating that grain size is not a key factor contributing to the low resistivity contrast.Overall,formation water salinity in the this area exceeds 15 000 mg/L,resulting in generally low resistivities for both gas and water zones,which forms the fundamental background for the low contrast.Clay minerals in the Qulitag structural belt are primarily illite and interlayered illite-montmorillonite(I/M).In water-bearing zones,I/M mixed layers and fibrous illite narrow and complicate pore throats,blocking free water conduction pathways and increasing water zone resistivity.In gas zones,higher clay content enhances water adsorption capacity,reducing gas zone resistivity.Thus,clay content plays a crucial role in generating low resistivity contrast.The pore structure of the study area exhibits a typical bimodal distribution,characterized by a high proportion of fine pores and throats.This complex pore structure leads to elevated bound water saturation.Increased bound water saturation improves the conductive network,lowering gas zone resistivity and further reducing the resistivity difference between gas and water zones:a key factor in forming low resistivity contrast.Based on this understanding,we developed a continuous pore structure calculation model using NMR and mercury intrusion data combined with well logging curves,and subsequently established models for bound water saturation and total water saturation based on pore structure.The difference between total water saturation and bound water saturation enables accurate identification of fluid types.Validation using production data from newly drilled wells confirms that this method achieves an 86.7%accuracy rate in identifying fluid properties.
As an unconventional oil and gas resource with huge reserves,oil shale has attracted much attention.Studying the frequency dispersion and temperature sensitivity of the complex resistivity of oil shale is conducive to deepening the exploration and in-situ heating recovery technology of oil shale.In this study,experiments are conducted by sealing with soft silicone and heating in a water bath to measure the impedance spectra of the Chang7 oil shale in the temperature range of 25～95℃and the frequency range of 4 Hz～5 MHz.The experimental data are fitted with the second-order Cole-Cole model,and a systematic study is carried out on the parameter sensitivity and the influence of temperature on the complex resistivity characteristics.The results show that:① The complex resistivity dispersion of oil shale shows a dual polarization mechanism.The real part of the resistivity decreases stepwise with multiple slopes as the frequency increases,and the imaginary part of the resistivity shows a double-peak evolution characteristic.② The increase in temperature leads to an increase in ion mobility,a shortening of polarization relaxation time,and a weakening of energy dissipation.The absolute values of the real and imaginary parts of the complex resistivity decrease overall,and both the first polarization frequency and the interface polarization frequency increase exponentially with temperature.③ The first-order derivative of the real part of the resistivity with respect to the logarithm of the frequency is highly correlated with the imaginary part of the resistivity.The attenuation rate of the real part of the resistivity is the fastest when the polarization intensity is the highest.④ The determination coefficient R2 of the second-order Cole-Cole model fitting is generally higher than 0.98.The zero-frequency resistivity and relaxation time in the model parameters decrease significantly with increasing temperature,the polarization rate decreases in the high-temperature section,and the frequency coefficient fluctuates with temperature.The conclusion is that the second-order Cole-Cole model can effectively describe the dual polarization characteristics of oil shale.The research results deepen the understanding of the coupling relationship between the electrical properties of oil shale and temperature,and can provide a reference for the geophysical exploration and development of oil shale.
The Lianggaoshan formation sand-shale interbedded shale oil reservoirs in the Pingchang area, northeastern Sichuan basin are abundant in oil and gas resources. However, such reservoirs feature developed sand-shale interbeds, complex lithologic associations, and strong differences in interlayer rock mechanical properties and in-situ stress, making the existing fracability evaluation methods ineffective in guiding the fracturing interval selection and process design. Based on triaxial rock compression tests, Brazilian disc tests, differential strain tests and other laboratory experiments, this paper systematically analyzes the characteristics of rock mechanical properties, brittleness index, differential coefficient of two horizontal principal stresses, and equivalentizes the reservoir as a vertical transverse isotropy (VTI) medium. A fracability evaluation index tailored to the sand-shale interbedded shale oil reservoirs in the study area is established using the support vector regression algorithm, and the production performances of intervals with different fracability levels are compared. The results show that: ① The elastic modulus of the Lianggaoshan formation sand-shale interbedded shale oil reservoirs in the study area ranges from 10 to 35 GPa, and the Poisson's ratio ranges from 0.14 to 0.30. Both the elastic modulus and Poisson's ratio parallel to the bedding direction are larger than those perpendicular to the bedding direction, indicating significant anisotropy. The elastic modulus and Poisson's ratio perpendicular to the bedding direction are the dominant factors affecting reservoir fracability. ② Sandstone intervals with low minimum horizontal principal stress and high differential coefficient of two horizontal principal stresses, as well as sand-shale transition zones adjacent to shale intervals, exhibit superior fracability. The large differential coefficient of two horizontal principal stresses strengthens the cross-layer propagation capacity of artificial fractures, which effectively connects the sand-shale interbeds. ③ High tensile strength raises the initiation pressure for reservoir stimulation, prevents fracturing fluid leakage, reduces energy loss during fracture cross-layer propagation, and keeps fractures more prone to staying open during propagation after fracturing, which is conducive to the formation of stable flow conduction channels. ④ Sandstone intervals and sand-shale transition zones with high brittleness index, large differential coefficient of two horizontal principal stresses, high tensile strength and low minimum horizontal principal stress present high fracability indexes, favorable fracturing effects and high oil and gas production rates. Case verification demonstrates that the established log-based fracability evaluation method accurately characterizes the propagation potential of hydraulic fractures, and is highly consistent with the fracturing monitoring data in the post-fracturing flowback and production stage. The research results provide a basis and guidance for the design of differentiated fracturing schemes and the optimization of fracturing intervals in the development of sand-shale interbedded shale oil reservoirs.
To address the problems of limited prediction accuracy and high inversion uncertainty of low signal-to-noise ratio echo data when traditional nuclear magnetic resonance (NMR) logging T2 spectra are converted into pseudo-capillary pressure curves for characterizing pore-throat parameters of tight sandstone reservoirs, and to provide more accurate reservoir pore-throat parameter support for oil and gas exploration and development, carbon dioxide geological storage, and other applications, the study adopts the time-domain analysis technology of NMR logging. Three groups of Laplace transform function pairs are constructed, and the research is conducted by combining numerical simulation, core experiments, and well logging data processing. The research results are as follows. First, three types of time-domain parameters, namely P1, P2 and P3, are successfully extracted directly from NMR echo data, thus avoiding the T2 spectrum inversion process. Numerical simulation verification indicates that under the signal-to-noise ratios of 30 and 5, the root mean square errors (RMSE) of characteristic parameter calculation by this method are lower than those by the T2 domain analysis method, with more significant advantages under low signal-to-noise conditions. Second, after optimization via core experimental data analysis, the correlation coefficients (R2) between P2 and the average pore-throat radius, median pore-throat radius, and maximum pore-throat radius reach 0.92, 0.81, and 0.89, respectively. These values are significantly superior to those corresponding to P1 and P3. Third, for the pore-throat parameter characterization model established based on P2, the R2 for the prediction of the above three key parameters are 0.90, 0.76, and 0.82, with the respective RMSE values being 0.03 μm, 0.03 μm, and 0.15 μm. The prediction accuracy of this model outperforms that of traditional pseudo-capillary pressure curve conversion methods, including linear conversion, piecewise power function, and extended power function approaches. Finally, processing results of field logging data reveal a high degree of consistency between the predicted pore-throat parameters and the measured results from core experiments. This method thus enables the continuous characterization of pore-throat parameters in tight sandstone reservoirs. It is concluded that the novel method based on time-domain analysis of NMR logging exhibits high stability and excellent anti-noise interference capability. It provides a reliable technical tool for the characterization of pore-throat parameters in unconventional reservoirs such as tight sandstones, and holds important reference value for reservoir quality evaluation of complex reservoirs.
