Electrical Image Logs Research Articles

Optimising Neural Networks for Enhanced Fracture Density Prediction in Surrounding Rock of Coalbed Methane Reservoir

ABSTRACTFractures influence the mechanical strength of coal roof and floor, constraining the design of hydraulic fracturing for coalbed methane production. Currently, the predominant approach involves the integration of petrophysical logging with machine learning for fracture prediction. Nevertheless, challenges exist regarding the model's accuracy. In this study, we present a novel approach to predict fracture density. Our method optimises a back‐propagation (BP) neural network and utilises principal component analysis for feature extraction. We employ logging parameters (density, compensated neutron and acoustic time difference) obtained from Shouyang Block well SY‐1 and fracture density data from electrical imaging logging to construct the FVDC model's dataset. The BP neural network model is optimised using the Sparrow Search algorithm and Tent Chaotic Mapping. The results demonstrate a substantial enhancement over the BP neural network model, with reductions of 80.102% in mean absolute error, 94.182% in mean square error, 75.879% in root mean square error and 79.764% in mean absolute percentage error. When considering accuracy, the optimised model (97.098%) surpasses the support vector regression model (96.478%), the random forest model (94.404%) and the BP neural network model (85.657%). Scalability testing for the optimised model was conducted using data from well SY‐2, yielding a remarkable prediction accuracy of 96.775%. This performance exceeds that of the BP neural network (with an accuracy of 85.102%), as well as the random forest and support vector regression models (with accuracies of 91.234% and 90.384%, respectively). These results underscore the potential of well logging and machine learning in FVDC prediction.

Image Restoration of Electrical Well Logging Based on Fourier Convolution

Imaging logging is an important technical means in logging evaluation of complex reservoirs. Through imaging logging, a two-dimensional image of the resistivity distribution around the well can be obtained, which can be used to evaluate the development of well wall fractures and caves and the formation sedimentary structure. However, due to the characteristics of resistivity imaging logging instruments, blank strips will appear on the resistivity logging image, which increases the difficulty of computer processing of electrical imaging data. The current image repair method and the existing neural network image repair method are not effective enough when the part to be filled is large. Therefore, there is an urgent need for an intelligent repair method based on deep learning. Based on a fast Fourier convolution-based imaging logging image blank strip filling network, this paper constructs the electrical imaging logging images of the southwest oil and gas field as a data set, and trains a new deep learning algorithm for intelligent filling of blank strips in imaging logging images based on fast Fourier convolution. The time of various algorithms is compared. The results show that the algorithm has a better repair effect in imaging logging images with large strip widths, and the repair efficiency is significantly improved. This method can realize the rapid, accurate and intelligent repair of blank strips in imaging logging, achieve the rapid generation of full-borehole images, and solve the difficulties in obtaining full-borehole images.

Lithology identification using electrical imaging logging image: A case study in Jiyang Depression, China

Lithology identification plays a significant role in stratigraphic evaluation and geological analysis. Traditional lithology identification method is by modeling the relationship between well logging and lithology. However, well logging are not always sufficient to identify lithology since sometimes the curves are similar for different lithologies. Recently, electrical imaging logging image (EILI) with high resolution plays an increasingly important role in logging interpretation since EILI can intuitively reflect the characteristics of lithology. Unlike traditional lithology identification method by using well logging, in this paper, we propose a novel multi-dimensional automatic lithology identification method by applying deep learning to EILI. First, Filtersim algorithm is employed to fill the blank strip of the EILI. Then, an integrated convolutional neural networks (CNNs) model is designed to extract the resistivity feature, texture feature, and holistic feature of the EILI, respectively. Specifically, the integrated CNNs model can realize automatic recognition for different geological structures (massive, bedded, lamellar) and lithology (mudstone, sand-mudstone, lime-mudstone). Finally, lithology identification can be achieved by combining with multi-dimensional features. The efficacy of proposed integrated model is validated experimentally on the EILI of shale oil reservoir in the Jiyang Depression of China. Experimental results show the effectiveness and superiority of the integrated CNNs method for lithology identification.

Dipole acoustic remote detection (ARD) logging and its application in fracture-cave reservoir

Abstract Oil and gas from deep reservoirs are mostly enriched in the fracture-cave reservoirs. For this reason, fine evaluation of large-scale fracture-cave and effective detection of small scale fracture-cave are the key issues to be solved in the exploration of fracture-cave type carbonate reservoirs. The acoustic remote detection logging technology is based on acoustic reflection method to continuously image the formation around the well. The cross dipole array sonic data can be processed to image geological anomalies within a range of about 20 meters outside the well. Dipole S wave remote detection has the remarkable advantages of low emission frequency and larger investigation depth, but it has 180 degrees’ azimuthal ambiguity. This study is based on previously developed far field dipole S wave remote detection tool, finite difference is performed on formation models with and without are obtained for representative fracture-cave formations and remote detection processing and interpretation software is updated. These are applied on a well in the northwestern area. Through the comprehensive analysis of remote logging, conventional logging, electrical imaging logging and seismic data of the fracture-cave geological anomalies, a fine picture of the morphology and development of the geological anomalies from the near wellbore to the far end is formed

A Morphological Method for Predicting Permeability in Fractured Tight Sandstone Reservoirs Based on Electrical Imaging Logging Porosity Spectrum

Summary Permeability is a crucial parameter in formation evaluation, which reflects reservoir fluid mobility and directly influences subsequent development and production. In conventional to tight sandstone formations without fractures, numerous methods have been developed to predict permeability. However, permeability prediction accuracy based on the current method is low in fractured tight sandstone reservoirs due to the influence of heterogeneity, and little research is focused on permeability evaluation in such reservoirs. Conventional wireline and nuclear magnetic resonance (NMR) logging lose their role in fracture parameter evaluation, whereas electrical imaging logging is available for reflecting fracture information. Hence, we adopt electrical imaging logging, instead of conventional wireline and NMR logging, to predict permeability in fractured tight sandstone reservoirs. In this study, we propose a morphological method for permeability prediction using electrical imaging logging. Initially, we process the electrical imaging logging data to obtain the porosity spectra and determine the peak of the primary porosity spectrum. We then extract the mean and standard deviation of the primary porosity spectrum based on its distribution morphology. Meanwhile, we also use the normal distribution function to fit the primary porosity spectrum and accumulate the amplitudes of the original and fitted porosity spectra, respectively. When the cumulative amplitude of the fitted spectra stabilizes, the corresponding porosity indicates the boundary between primary and secondary pores. This boundary allows us to calculate the proportion of primary pores to total pores. Permeability in fractured tight sandstone formations is influenced by both primary and secondary pores, showing a strong correlation with the proportion of primary porosity. Finally, we establish a permeability prediction model based on total porosity and the proportion of matrix pores. The reliability of our established permeability evaluation model is confirmed by comparing the predicted permeabilities with core-derived results in the Triassic Chang 63 Member of the Jiyuan area in the Ordos Basin. In unfractured formations, the predicted permeability based on our raised model closely matches the results derived solely from total porosity. In fractured formations, however, the calculated permeability using our proposed model aligns more closely with the core-derived result, while predictions based on current and NMR models tend to underestimate permeability.

Automatic Geological Facies Analysis in Crust-Mantle Transition Zone

The Oman Drilling Project was conducted as a part of the International Continental Scientific Drilling Program (ICDP) from 2017 to 2018, and several boreholes, including four across the crust-mantle transition zone, were drilled in the program (Matter et al., 2019; Kelemen et al., 2020; Takazawa, 2021). A full suite of slim and conventional wireline logs, as well as high-resolution electrical borehole image and geochemical spectroscopy logs (Ellis and Singer, 2007; Liu, 2017), were acquired near the coring borehole. Full core samples were acquired from the coring boreholes, and various core analyses were conducted manually with significant time and effort to create a detailed core description. Identification of geological facies is crucial to understanding the complicated crust-mantle transition zone. Being able to achieve this facies recognition from available logging data using an automated method would optimize the operation cost and analysis time. This would be useful for the future scientific ultradeep ocean drilling Mohole to Mantle (M2M) project (Umino, 2015; Moe et al., 2018) planned by the International Ocean Discovery Program (IODP). We propose an automatic geological facies analysis (FaciesSpect) method using borehole images and other petrophysical log data on the Oman Drilling Project. Among the available logging data, borehole image (resistivity type with dynamic color scaling) and two log curves (Fe and Ca) from geochemical spectroscopy log data were selected for the automatic facies analysis. Fifteen clusters were classified from the selected log data using the proposed approach. The cluster distribution trend was consistent with cuttings and core lithologies and indicated three major lithology zones: dunite, gabbro, and harzburgite. Two automatic facies analysis methods, class-based machine learning and heterogeneous rock analysis, were performed to compare the results with those of FaciesSpect. These are well-established methods that can validate the newer FaciesSpect method result. The class results of the three different methods were compared. They are matched at major lithology change boundaries. FaciesSpect class results calibrated by core data could be matched not only with core lithology changes but also with texture changes, such as massive, layered, severely altered, deformed, and fractured, due to the advantage of using borehole image data as one form of input. In this case study, we applied the automatic facies analysis to a complicated scientific drilling well in the crust-mantle transition zone for the first time. The result successfully identified different lithologies, such as dunite and harzburgite, which have a high potential for hydrogen generation and are important resources for emissions-free renewable energy. We validated that the FaciesSpect method is useful for rapidly understanding the overall lithology and texture trends such as fractured, deformed, and massive intervals. The FaciesSpect method can also save analysis time and provide a fit-for-purpose result for different objectives beyond petroleum evaluation in a way that does not rely on the individual interpreter’s experiences. The high-resolution borehole image and geochemical spectroscopy logs are crucial inputs for automatic facies analysis in this study.

Research on quantitative inversion characterization of high-definition electrical imaging logging in oil-based mud based on backpropagation neural network and multiple population genetic algorithm-Levenberg-Marquardt algorithm

Electrical imaging logging in oil-based mud (OBM) has been developed for some time and is gradually playing an important role in the description of deep carbonate and shale reservoirs. Quantitative characterization of reservoir rock parameters such as resistivity is one of the most innovative developments in this field. The development of this technology needs to address and resolve four core issues: a wide range of parameter variations, removal of mud-cake influence in low-resistivity formations, dielectric rollover in high-resistivity formations, and multifrequency dielectric dispersion effects. To address the aforementioned issues, the joint use of a backpropagation neural network (BPNN) and the multiple population genetic algorithm (MPGA)-Levenberg-Marquardt (LM) algorithm for high-resolution quantitative imaging is developed. First, the numerical simulation is used to calculate the well-logging response data under the influence of multiple parameters, thereby establishing a forward response database. Then, within the forward response database, the instrument response function is fitted using BPNN, to compress the data volume. Next, based on the fitted response function, an inversion method for three parameters, including reservoir rock resistivity, permittivity, and plate standoff, is established using the LM algorithm optimized with MPGA. The results indicate that the use of a three-layer BPNN enables rapid and accurate calculation of the electrical imaging logging response in OBM. The calculation of a single point only requires 0.1 ms with an accuracy of more than 99%. The MPGA-LM algorithm exhibits stronger stability and improved inversion accuracy, with a single point inversion time of only 2 ms, and contributes to the high-definition quantitative description of electrical imaging logging in OBM, which is important in characterizing formation structures, distinguishing formation fractures, etc.

Parameter evaluation method of tight carbonate reservoir using electrical imaging pores diameter spectrum

Currently, high-yield gas reservoirs have been discovered by exploring Lower Paleozoic tight carbonate oil and gas reservoirs in the Changqing area. These reservoirs, however, are impacted by subsequent karstification and diagenesis processes, leading to the formation of complex pore spaces with varying pore diameters ranging from microns to millimeters. Conventional well curves, with a resolution at the decimeter level, can only provide an overview of the reservoir porosity, making it challenging to evaluate the pore structure. On the other hand, electrical imaging logging data has a sampling interval of 0.1 inches and a resolution as fine as 5 mm, providing valuable information for pore structure evaluation in tight reservoirs. Since formation resistivity is influenced by factors like porosity, mud filtrate resistivity, and cementation, the cementation index serves as a reflection of the pore structure. Therefore, by calibrating the cementation index using data obtained from experiments at different scales, the pore structure parameters of tight carbonate reservoirs can be evaluated. This study conducted a comparison between numerous core experiments at various scales and both conventional and imaging log data. A method was proposed for extracting pore size distribution characteristics and calculating the pore size spectrum based on electrical imaging logging, ultimately leading to the establishment of a quantitative evaluation model for the pore structure of tight carbonate reservoirs. The results demonstrated improved accuracy and consistency in interpretation, as confirmed by well test data.

A new method for quantitative evaluation of shale laminae using electrical image logging

Shale oil reservoirs are generally characterized by complex mineral compositions, rapid lithofacies changes, and thin laminae. Explorations have confirmed that the type and density of shale laminae significantly influence reservoir quality, highlighting the importance of accurately identifying these laminae through well logging for effective shale reservoir evaluation. Presently, relevant technologies primarily focus on the qualitative identification of shale laminae using vertical slab images from image logs. However, influenced by the complex borehole conditions and image logging quality, this approach is less effective in identifying millimeter-scale laminae. This study proposes a new method for achieving high-resolution slab images and quantitatively evaluating the laminae using electrical image logs. The new method effectively improves the processing accuracy of slab images by delicately flattening and aligning the button electrode curves derived from electrical image logs point by point. Meanwhile, it allows for the accurate quantitative evaluation of the lamina number through precise identification of peaks and troughs in microelectrode curves. As demonstrated by the applications in shale oil reservoirs in the Gulong area in Daqing and the Ganchagou area in Qinghai, the proposed method can significantly improve accuracy compared to traditional slab images. Furthermore, the lamination index calculated using this method is highly consistent with the lamina number observed in cores. This study provides a new technical method for the quantitative lamina evaluation and rock structure analysis of shale reservoirs.

A novel evaluation method of dolomite reservoir using electrical image logs: The Cambrian dolomites in Tarim Basin, China

The Cambrian dolomites in Tarim basin are important hydrocarbon exploration targets. However, these ancient dolostone reservoirs are deeply buried, and the lithology and reservoir space are complex, therefore the logging evaluation of reservoir effectiveness is difficult. This study analyzes the log response characteristics using core data and electric image logs, and divides the dolomite reservoir into four types: pore type, fracture type, vug type and fracture-vug type. Among them, the vug type and fracture-vug type are the main reservoir types with the highest industrial production. Then, this study innovatively proposes the method of point-by-point calibration of electrical image log, which avoids the difference in response characteristics of various electrical image log instruments, and reduces the uncertainty caused by human factors of conventional calibration. The point-by-point calibration method uses local data for comparison and calculates calibration coefficient automatically. The calibrated electrical image log data can maintain better matching and consistency with conventional resistivity in high and low resistivity simultaneously, and it is more truly reflect the borehole-wall resistivity, consequently, can establish a foundation for subsequent reservoir evaluation. Finally, according to the calibrated electrical image log data and the 360° roll-scan core photos, the vugs in-plane porosity on electrical image, the vug connectivity and the porosity spectrum are used to identify reservoir types and evaluate reservoir effectiveness. The vugs in-plane porosity on electrical image can truly reflect the development of fractures and vugs, and quantitatively evaluate the vugs and fractures of the whole borehole-wall by calibrating with core. The vug connectivity index can indicate the isolated vugs and connected vugs, and quantitatively evaluate the connectivity of vugs and fractures, in addition the porosity spectrum can reflect the pore structure of reservoir and identify the reservoir with secondary porosity. The coupling of the three methods can accurately identify high-quality dolomite reservoirs with vugs and good connectivity. This novel technology adopts the idea of classification and identification before quantitative evaluation and establishes a new bridge from qualitative analysis to quantitative evaluation. The method proposed helps improve the interpretation accuracy and coincidence rate and provides a reference for the comprehensive evaluation of ultra-deep dolomite reservoirs in the Tarim basin and worldwide.

New Insights into Fracture Porosity Estimations Using Machine Learning and Advanced Logging Tools

Fracture porosity is crucial for storage and production efficiency in fractured tight reservoirs. Geophysical image logs using resistivity measurements have traditionally been used for fracture characterization. This study aims to develop a novel, hybrid machine-learning method to predict fracture porosity using conventional well logs in the Ahnet field, Algeria. Initially, we explored an Artificial Neural Network (ANN) model for regression analysis. To overcome the limitations of ANN, we proposed a hybrid model combining Support Vector Machine (SVM) classification and ANN regression, resulting in improved fracture porosity predictions. The models were tested against logging data by combining the Machine Learning approach with advanced logging tools recorded in two wells. In this context, we used electrical image logs and the dipole acoustic tool, which allowed us to identify 404 open fractures and 231 closed fractures and, consequently, to assess the fracture porosity. The results were then fed into two machine-learning algorithms. Pure Artificial Neural Networks and hybrid models were used to obtain comprehensive results, which were subsequently tested to check the accuracy of the models. The outputs obtained from the two methods demonstrate that the hybridized model has a lower Root Mean Square Error (RMSE) than pure ANN. The results of our approach strongly suggest that incorporating hybridized machine learning algorithms into fracture porosity estimations can contribute to the development of more trustworthy static reservoir models in simulation programs. Finally, the combination of Machine Learning (ML) and well log analysis made it possible to reliably estimate fracture porosity in the Ahnet field in Algeria, where, in many places, advanced logging data are absent or expensive.

Prediction Method of Coal Texture Considering Longitudinal Resolution of Logging Curves and Its Application-Taking No. 15 Coal Seam in the Shouyang Block as an Example.

The quantitative identification of the coal texture is of great importance as a crucial parameter for coalbed methane (CBM) reservoir evaluation. This study combined drilling core data, electrical imaging logging data, and four conventional logging data, namely, compensation density (DEN), natural γ (GR), deep lateral resistivity (RD), and acoustic time difference (AC), to achieve accurate inversion of coal texture in the Shouyang Block. Meanwhile, wavelet analysis and Fisher discriminant analysis were introduced to the inversion process to further improve the accuracy. Through the utilization of software packages, such as Matlab and SPSS, the establishment of the coal texture logging interpretation chart of the No. 15 coal seam in the Shouyang block was successfully realized. The outcome of this comprehensive study reveals that the coal texture logging interpretation chart is an effective tool for the identification and classification of each coal texture and gangue. Moreover, the validity and reliability of this method were tested and confirmed using wells CS-8 and CS-9 in the region, achieving an accuracy of 97.1 and 93.2%, respectively. This innovative method has significant prospects for predicting and evaluating the coal texture in the Shouyang Block, which can be further applied to other regions.

Quantitative characterisation of fracture connectivity from high-resolution borehole image logs

Fractures are fundamental structural elements in brittle rocks. They have a primary role in transporting geofluids, particularly in basement and carbonate rocks, where they have been the focus of reservoir characterisation and modelling for decades. Fracture networks are often described by statistical analysis of their geometrical attributes such as orientation, length and aperture. However, the spatial arrangement (topology) of fractures/fracture sets and its temporal evolution is equally important to better understand physical attributes of rocks. How much is a fracture network connected? Can this be quantified? Does connectivity change in different locations, if yes what is the reason for it? What is the brittle strain distribution in a fracture network? How much are fracture density and connectivity influenced by mechanical stratigraphy?This paper attempts to address these relevant questions and test quantitative fracture connectivity analysis on high resolution electrical and ultra-sonic borehole image logs in an appraisal well (Total depth, TD: 2195.36 mMD), drilled into the Utsira High basement, in the extensional Norwegian North Sea rift basin. Fracture connectivity and fracture size attributes were investigated in Permian Zechstein carbonates and Ordovician-Silurian granitic basement and separately for defined fracture zones at the interval of 2050–2195 mMD. The average degree of fracture/fault connection is good (D = 2.6) for the sediments and excellent (D = 3.5) for the basement. Accordingly, the block intensity (R22) is 0.4 for sediments and 0.2 for basement, which imply a moderately and strongly disintegrated host rock, respectively. There is a large fracture connectivity variation in different fracture zones, i.e. completely isolated fractures (Zone 1) vs highly interconnected and strongly disintegrated basement (Zones 9–10). The connectivity variation is governed by lithology, mechanical properties (stiffness), strain distribution and presence of weakness zones (faults). The study also highlights some possible applications of fracture connectivity results to reservoir modelling and future follow-up trends in quantitative spatial characterisation of fracture networks.

Automatic fracture detection from the images of electrical image logs using Mask R-CNN

This paper presents a novel approach for automatically detecting fractures from electrical image logs using a state-of-the-art object detection and segmentation method, i.e., the Mask Regional Convolutional Neural Network (Mask R-CNN). First, we apply image processing algorithms of inpainting and augmentation to generate the basic dataset. The fractures in those processed images are properly identified and labeled. The processed images with the annotated fractures will be used as input data for the training and testing of the Mask R-CNN model. Subsequently, the input data are randomly split into training dataset, validation dataset, and testing dataset. The Mask R-CNN model is trained using the training and validation datasets. The performance and accuracy of the trained model is tested by comparing the predicted fractures from the testing dataset to the actual fractures that are identified manually. The testing results demonstrate that the trained Mask R-CNN model has precision and recall values of 96% and 92% in detecting fractures from the image logs. Last, we further validate the proposed approach by comparing the fractures detected using the developed Mask R-CNN model against those identified from three core samples. The comparison also indicates a high accuracy of the Mask R-CNN model.

Evaluation and engineering applications of the in situ stress state of deep tight sandstone reservoirs in the Xujiahe Formation of the Yingshan-Longgang in the central Sichuan Basin

Introduction: The Xujiahe Formation in the Yingshan-Longgang area of the Sichuan Basin has abundant tight oil and gas resources. However, the complex geological conditions and insufficient understanding of the magnitude and direction of in-situ stresses severely limit exploration and development of oil and gas resources in the region. This has made it difficult to carry out hydraulic fracturing and horizontal well deployment in the region.Methods: Based on experimental analyses, including acoustic emission experiments, wave velocity anisotropy experiments, and paleomagnetic experiments, this study evaluated the magnitude and direction of in-situ stresses, while also combining electrical imaging logging and acoustic wave logging data. This provides a basis for hydraulic fracturing in the research area.Results and Discussion: The experimental results indicate that the three-way stress state of the Xujiahe Formation is a strike-slip stress state, and the three-way stress value gradually increases with increasing burial depth. The Longgang area is particularly affected by the depth. The average azimuth angle of the maximum horizontal principal stress in the study area is N100°E, while tectonic deformation may cause a counterclockwise deviation of the maximum horizontal principal stress azimuth in the Yingshan area. Considering the influence of changes in the magnitude of in situ stress in the longitudinal direction on hydraulic fracturing extension mode, it is recommended to select the middle and upper sections of the Xu II for fracturing and transformation. Similarly, considering the distribution of natural fractures and the direction of the in situ stresses, it is suggested that the deployment azimuth angle for horizontal wells be between N20°–30°E.

Prediction of lithology in lacustrine carbonates using well logs: The Cretaceous Barra Velha Formation in Santos Basin, offshore Brazil

Abundant hydrocarbons have been produced from the pre‐salt lacustrine carbonates of the Cretaceous Barra Velha Formation in the Santos Basin of offshore Brazil. The highly heterogeneous lithology and pore systems make it difficult to predict the reservoir quality via petrophysical well logs. Core description, computed tomography (CT) scanning, thin sections and wireline well logs are utilized to characterize the lithology, pore spaces are reservoir quality of the Cretaceous Barra Velha Formation pre‐salt lacustrine carbonates in the Santos Basin. The results show that the lithology mainly includes shrubstone, grainstone, laminated micrite and massive micrite. The pore systems are dominated by secondary pores, such as intergranular pores, dissolution pores and intercrystalline pores as well as modified primary pores. Geological well logs (conventional well logs, electrical and sonic image logs and nuclear magnetic resonance [NMR] logs) are integrated to build the predictable models of the carbonate reservoirs. The shrubstone, which is dominated by intergranular pores, has the highest reservoir quality, which can be derived from NMR logs. Grainstone contains both primary intergranular pores and abundant secondary dissolution pores. The laminated and massive micrite contain only micropores and therefore have the lowest reservoir quality. Lastly, conventional well logs integrated with electrical and sonic image logs are utilized to predict the distribution of lithology and reservoir quality in single wells, and the results are testified by the NMR log data. The shrubstone and grainstone are future exploration targets of the Cretaceous Barra Velha Formation in the Santos Basin. The results are hoped to give insights into the petrophysical behaviours of various lithologies, and provide theoretical and technical guidance for reservoir evaluation and prediction.

Evaluation of irreducible water saturation by electrical imaging logging based on capillary pressure approximation theory

One of the most important metrics for evaluating oil and gas reservoirs is irreducible water saturation (Swir). The completion of oil and gas reservoir development tasks, such as fluid identification, productivity prediction, and water-flooded reservoir discrimination, requires the accurate evaluation of Swir. Nuclear magnetic resonance (NMR) logging can reflect the intricate pore structure of reservoirs, so it offers a natural advantage in the evaluation of Swir, but its high measurement cost leads to infrequent use. In geophysical logging, electrical imaging logging can obtain pore size distribution information at a lower cost. Hence, a method based on electrical imaging logging data is proposed to predict Swir. This method not only has a high degree of accuracy but is also cost-effective. First, the pore characteristics of the bound fluid were analyzed with the mercury injection capillary pressure curve. Based on the capillary pressure approximation theory, the reverse cumulative curve of the porosity spectrum was derived. And the physical meaning of its “inflection point” was clarified. Then, the resistivity of the formation containing just irreducible water was computed using the conversion between the core displacement pressure and resistivity. Finally, based on Archie's formula, the Swir at each depth of the reservoir was predicted. By using ultradeep carbonate reservoir logging data from the Yuanba gas field, the model was validated. The results demonstrated that the calculated values using this method agreed with the measured values from the cores. Compared with the core fitting method, the average absolute error is reduced by 2.61%, and the average relative error decreases by 12.0%. If NMR logging information is not present, this method serves as an effective supplement to accurately predict Swir.

Summary of research methods for porosity and permeability of coalbed methane reservoir

In order to serve the research on the physical properties of coalbed methane reservoirs and improve the prediction effect of porosity and permeability of coal reservoirs, this paper, based on a large number of literature research, summarizes the concepts, influencing factors and prediction methods of coal seam porosity and permeability. The research shows that the main factors affecting the porosity of coalbed methane reservoir are coal petrographic composition, metamorphic degree, coal body structure and buried depth, compaction, tectonism, etc; The main influencing factors of permeability include component content, coal rank, coal body structure and in-situ stress, buried depth of coal seam, natural fracture of coal seam, matrix shrinkage, Klinkenberg effect, etc. The porosity prediction of coalbed methane reservoir generally adopts true and apparent relative density method, in addition, there are multiple regression method based on logging information, nuclear magnetic resonance logging method, etc; The measurement methods of CBM reservoir permeability mainly include the calculation method based on conventional logging, triaxial stress experimental research method, joint prediction method of nuclear magnetic resonance and electrical imaging logging, dynamic data analysis method and numerical simulation method. The development of coal seam porosity and permeability prediction technology needs innovative breakthroughs in hardware and software.

Evaluation method for in-situ stress orientation using wave anisotropy and electrical imaging logging data: A case study from the Longmaxi Formation in Yongchuan area, Sichuan Basin

In-situ stress orientation is a key parameter in studying oil and gas migration and accumulation, analyzing the stability of borehole wall during drilling, carrying out horizontal well design, fracturing reconstruction and well pattern layout in water injection development. Accurately predicting the in-situ stress orientation of reservoirs is of utmost importance for oil and gas exploration and development. It is the most effective and commonly used method to evaluate the in-situ orientation by using the induced fracture and breakout fracture of imaging logging data. However, in cases where effective induced fracture or breakout fracture cannot be formed during drilling, the in-situ orientation cannot be evaluated by imaging data alone. Taking the shale reservoir of Longmaxi Formation in Yongchuan area as an example, this paper presents a novel method for evaluating in-situ orientation. Firstly, the in-situ orientation is determined relative to the core sample marker line through the wave velocity anisotropy experiment. Secondly, the core sample is rotated to obtain an unfolded image of the core surface. The orientation of the core marker line in the electrical imaging is then determined by comparing the core image with the electrical imaging logging image. By doing so, the true orientation of the core can be obtained. This method aligns well with the in-situ stress orientation determined through induced fracture evaluation, addressing the limitation of electrical imaging data in evaluating in-situ stress orientation in plastic strata.

SVR-based weighted processing method for electrical imaging logging in oil-based mud

Electrical imaging logging in high-resistivity oil-based mud (OBM) has been one of the hotspots in petroleum exploration and development recently. Compared with the traditional electrical imaging logging in water-based mud (WBM), the core issue restricting the application of electrical imaging logging in OBM is that the mud-layer with high impedance prevents the emitted current from flowing into the formation and seriously disturbs the measurement of formation properties. Here, a SVR (support vector machine regression)-based weighted processing method is proposed to resolve the intractable problem and three key parameters are obtained simultaneously. First, the equivalent circuit model of electrical imaging logging in OBM is established and the drawback especially occurring in low-resistivity formation is analyzed. Then, a weighted coefficient applied to the mud-layer impedance is introduced for the quantitative calculation of formation resistivity, and the change of relative formation permittivity and mud-layer thickness can also be depicted with weighted processing. Next, a SVR model is established to inverse the weighted coefficient and then figure out the inversed parameters including the formation resistivity, the relative formation permittivity, and the mud-layer thickness. A stochastic parameter model is adopted to verify the validity of SVR model. Finally, two deviated formation models further test the advantage and robustness of SVR model. The results demonstrate that the SVR-based weighted processing method possesses a terrific performance and all the three parameters can be calculated simultaneously regardless of the borehole wall condition. This study will provide a new approach to the data processing and interpretation of electrical imaging logging in OBM.