Logarithmic Curve Research Articles

Modelling pressure dynamics of oil–gas two-phase flow in double-porosity media formation with permeability-stress sensitivity

Pressure dynamics can reflect the basic characteristics of fluid flow through underground porous formation. In this research, the oil–gas two-phase flow model for a double-porosity media formation with permeability-stress sensitivity was first established for three kinds of outer boundaries. The unified governing equation was deduced by utilizing the H function. The nonlinear mathematical model in consideration of permeability-stress sensitivity was linearized by implementing the canonical perturbation transformation and then solved by using the Laplace transformation. After that, a sequence of typical log–log curves of pressure dynamics influenced by various model parameters were plotted and analyzed. These curves reflect the typical characteristic of a V shape caused by the inter-porosity fluid flow from matrix toward natural fractures. Oil saturation and permeability-stress sensitivity coefficient have much influence on the pressure dynamics. Ultimately, the established model of oil–gas two-phase flow was validated through a well-test fitting interpretation for a real condensate gas well in a sandstone formation. This research can offer insights into the pressure dynamics dominated by the oil–gas two-phase flow in naturally fractured formations and the permeability-stress sensitivity effect.

Interfacial capacitance in lithium disilicate glass: Experimental factors and charge carrier density

AbstractThe formation of an electric double‐layer (EDL) is an important phenomenon for many research areas, including energy storage technology. Although EDL is well‐known in electrochemistry, most of the studies involve the characterization of liquid electrolyte/electrode interfaces, and only a limited number of studies in solid‐solid contacts, such as solid electrolyte/electrode interface are available. This paper employed electrochemical impedance spectroscopy (EIS) to systematically investigate the influence of experimental factors in the interfacial capacitance arising from the electrode polarization in a lithium disilicate glass/gold electrode interface. It analyzed the influence of a.c. input voltage amplitude, samples' roughness (mechanical and chemomechanical polishing) and thickness, range of applied frequency and temperature, and the number of impedance cycles. In short, it was found that an input voltage range of 15–60 mV is indicated to minimize potential electrochemical processes during electrode polarization, where the data is reproducible from the second measurement cycle onward. Smoother surfaces closely approximated ideal electrode spike behavior, with surface treatment exhibiting influence on interfacial capacitance values. Moreover, as expected, we observed an increase in relative permittivity values with increasing thickness, accompanied by decreased capacitance values. Finally, by employing optimal experimental conditions and analyzing the inflection frequency () of the versus log() curve, we determined that the ratio between effective charge carriers () and the total number of charge carriers () falls within the range of 5–12% between 130°C and 280°C.

Evaluation of Organic Carbon Logging in the Chang 7 Section of the Yanchang Formation, Huanjiang Area, Ordos Basin

During the early stages of shale oil exploration, challenges such as limited coring of source rocks and the discontinuous distribution of measured samples arise. Logging data can be used to quantitatively evaluate source rocks. Organic-rich source rocks typically exhibit characteristics such as high gamma radiation, low density, high sonic lag, high resistivity, and high neutron porosity on logging curves. This paper systematically introduces two quantitative evaluation methods for source rocks based on logging data: the Δlog R method and the BP neural network model. Corresponding prediction models were developed to forecast the organic carbon content of source rocks in the Chang 7 section of the Yanchang Formation in the Huanjiang area of the Ordos Basin. The predicted TOC (total organic carbon) data were copared with measured TOC data. The results indicate that both the ΔlogR model and the BP neural network model are effective for the study area, with the BP neural network model showing significantly better fitting performance than the ΔlogR model. The study also predicts the plane distribution of TOC content in the source rocks of the study area.

Intelligent Lost Circulation Monitoring Method Based on Data Augmentation and Temporal Models

Deep and offshore drilling operations face complex geological formations, uncertain formation pressures, and narrow safety density windows, making them susceptible to lost circulation risks. To address these challenges, this paper introduces an innovative, intelligent lost circulation monitoring model that incorporates geological lithology information. This model not only utilizes real-time drilling parameters, but also encodes geological information such as rock type as inputs to the model. By combining these key lithological features, the model can comprehensively assess wellbore stability and reduce the lost circulation risks. In this paper, the Conditional Tabular Generative adversarial network (CTGAN) model is used to enhance the data of small-sample risk data, which can effectively expand the data distribution space and improve the performance of the model. This paper conducts a comparative analysis of intelligent monitoring results using artificial neural networks (ANNs), long short-term memory (LSTM), and temporal convolutional networks (TCNs). The results show that the TCN achieves an identification accuracy of 93.7%. Furthermore, the analysis reveals that the inclusion of lithology information significantly enhances the model’s performance, resulting in a 7.1% increase in accuracy. The false alarm rate of the model can be reduced by 10.2%, considering the fluctuation of the logging curve caused by the on/off condition of the pump. This indicates that the introduction of lithology information and the condition of the pump on−off provide advantages in monitoring and identifying lost circulation risks, enabling a more precise assessment of wellbore stability and a reduction in lost circulation incidents. The method of lost circulation monitoring proposed in this paper provides an important safety guarantee for the oil drilling industry.

POLQ immunostaining behaves as a prognostic factor for pancreatic carcinoma.

DNA polymerase theta (POLQ) is a translesion synthesis polymerase essential for the repair of double strand breaks by the error-prone TMEJ (Theta Mediated End Joining) pathway. Although POLQ participates in maintaining genome stability, several studies have shown that its overexpression correlates with cancer progression and poor prognosis. Due to the fact that its role as a biomarker in pancreatic cancer remains unexplored, we aimed to study the usefulness of POLQ H-score as a prognostic factor in a pancreatic cancer patient cohort. We evaluated POLQ gene expression using a web-based tool to deliver gene expression profiling and interactive analyses based on TCGA and GTEx (GEPIA) and we examined the POLQ immunostaining in 152 biliopancreatic cancer surgical specimens using tissue microarrays. Association with survival was evaluated by Kaplan Meier curves and uni-multivariate Cox regression. GEPIA analysis showed statistical differences according to POLQ mRNA levels in Disease Free Survival (DFS) (log rank 0.023, HR 2.8, p=0.029) and Overall Survival (OS) (log rank 0.011, HR 3.1, p=0.016). For immunohistochemistry (IHC) evaluation, POLQ H-score was calculated, and showed statistical differences for OS in Kaplan Meier curves (log rank 0.001) and uni-multivariate analysis (HR 2.27; 95% CI 1.24-4.15, p=0.008). Our results indicate that POLQ is an independent prognostic factor in pancreatic cancer when analyzed by immunostaining, which is in agreement with the results shown by the POLQ gene expression analysis (GEPIA).

Difference of Sedimentary Characteristics and Main Controlling Factors Between Platform Margin Shoal and Ramp Shoal in M Formation of A Oilfield in The Middle East

Abstract The bioclastic limestone reservoirs of M Formation in A Oilfield mainly develop in the ramp shoal and platform margin shoal, but the sedimentary characteristics, spatial distribution and main controlling factors of the two types of shoals are significantly different. Based on the data of seismic, well logging, core, casting thin sections and analytical tests, the comparative analyses of lithology, petrophysics and spatial distribution of the two types of shoals are carried out, and the control mechanism of relative sea level rise and fall and paleogeomorphology on their development is clarified. It is showed that the two types of shoals can be subdivided according to hydrodynamic conditions and bioclastic types. The platform margin shoals are dominated by grainstone and gravel limestone, and the bioclastics are mainly rudist and echinoderms. The logging curve is funnel-shaped, and the seismic is patchy distribution. In the vertical direction, the grain size of shoals becomes coarser, and the sedimentary sequence of medium energy shoal—medium-high energy shoal—high energy shoal is developed. In the horizontal direction, the facies belts of different shoals are narrow, and they are distributed quasi-layered semi-continuously around high energy shoals. The ramp shoals are dominated by packstone and grainstone, and the bioclastics are mainly non-fixed bivalves and algae. The logging curve is micro-tooth box-shaped, and the seismic is sheet distribution. In the vertical direction, the grain size change of shoals is not significant, and the sedimentary sequence of medium energy shoal—medium-high energy shoal is developed. In the horizontal direction, the facies belts of different shoals are wide, and they are distributed layered continuously around medium-high energy shoals. Relative sea level change controls the energy of sedimentary environment and the type of shoal facies, and the high position of paleogeomorphology controls the formation and distribution of high energy shoals. When the relative sea level is high and the paleogeomorphology is gentle, the ramp shoal is developed. As the relative sea level drops, the hydrodynamic condition increases, and the difference in the accumulation speed of different shoals enhances the fluctuation of the paleogeomorphology, the ramp shoal gradually transitions to the platform margin shoal. The above research can provide reference for the exploration and development of such reservoirs.

Lag Plot: A Novel Method to Determine Viable Bacterial Cell Number via a Single OD Measurement.

Bactericidal activity is a valuable parameter which is considered and measured for antimicrobial compounds. The available standard protocol to evaluate bactericidal activity is based on the direct colony count. Colony counting requires serial dilution, plating, overnight incubation, and direct counting, which is time-and labor-intensive. In regard to eliminate direct plate count, novel techniques were developed based on the real-time growth monitoring which can come with some limitations and drawbacks. These drawbacks encourage us to develop a novel technique with simple procedure to determine viable bacterial cell count. In this procedure, real-time growth monitoring is not required. In fact, after an incubation time, the number of viable bacteria can be determined with a single OD measurement and through an equation. In this regard, four standard bacterial strains, including Staphylococcus aureus (ATCC 25923), Escherichia coli (ATCC 25922), Pseudomonas aeruginosa (ATCC 27853), and Bacillus subtilis (ATCC 23857), were cultured with descending inoculum densities (1.5 × 107 to 15CFU/mL) and growth curves were drawn. As expected, growth in the samples with lower inoculum densities recorded with longer lag phase. Also, a direct relation was observed between the recorded turbidities and initial cell counts. A logarithmic curve (lag plot) was obtained by plotting the OD, after 12-18h incubation, against initial cell counts. In all examined strains, R2 was calculated in the range of 0.96-0.99 which is acceptable value for coefficient of determination. Equation corresponding to the lag plot was obtained and called lag equation. This equation is applicable to calculate the number of viable cells in unknown samples simply by inoculation, incubation, and single OD measurement. Developed technique can be introduced as a simple substitution for labor- and time-intensive direct colony counting.

Geological characteristics and seismic identification of reservoirs in the Colorado Basin off the Atlantic coast of Argentina

Abstract The Colorado Basin is a hot exploration frontier basin conjugated with the southwest coast of Africa which has undergone four evolutionary stages: Carboniferous-Jurassic pre-rift, Jurassic-Early Cretaceous rift, Late Cretaceous transition and passive marginal drift since Paleocene. Major oil and gas discoveries have been made in the conjugated Orange Basin in recent years, including the Graff field and Venus field discovered in 2022 with recoverable reserves of 550 Mboe and 2.3 Bboe, respectively. This indicates that the basin has good oil and gas exploration potential. Based on the analysis of drilling data and seismic data, it is concluded that the basin has a mature oil-gas generation system and better reservoir conditions. The pre-rift and syn-rift source rocks are confirmed by 5 Wells with oil and gas shows in the shallow water shelf, and all have entered the hydrocarbon generation threshold. Reservoir distribution and trap-sealing conditions have become key factors in oil and gas exploration. this paper describes the reservoir characteristics in the drilling data, and extrapolates the seismic reflection characteristics of the same strata, and predicts the reservoir distribution in the unexplored area of the basin. The reservoir lithology and sedimentary facies are analyzed based on the gamma logging curves. Reservoir identification and reasonable prediction on seismic profile are mainly determined by seismic reflection amplitude and horizon calibration. The basin has three sets of reservoirs, namely, pre-rift Permian underwater fan sandstone, Lower Cretaceous alluvial fan sandstone of syn-rift period and Upper Cretaceous Colorado Formation shallow marine sandstone of transitional period. Among them, Colorado Formation sandstone reservoir has the best physical property conditions, with a maximum porosity of 32% and a thickness of 1000m. The Cretaceous and Paleogene turbidite sandstone of slope fan facies are potential reservoir in the deep waters, which mainly form litho-stratigraphic traps in the continental slope.

The invention relates to a method for simulating phase permeability curve based on conventional logging data

Abstract Phase permeability curve is the curve of relative permeability (effective permeability/oil phase permeability in bound water) and water saturation change obtained through core phase permeability experiment. It is an indispensable data in oilfield development calculation, dynamic analysis, determination of oil-water saturation distribution in reservoirs and other water-driven oil related calculations, and an important index for reservoir evaluation. The study of effective permeability and relative permeability is of great significance for controlling the uniform advance of oil-water or oil-gas boundary and rationally developing oil and gas fields. The only effective way to study the phase infiltration is the core phase infiltration experiment, but the number of lithology samples is very limited, so the phase infiltration in oilfield development and application is still in the estimation stage, the accuracy is low, and the practical application is not strong. In order to improve the applicability of the phase permeability curve, it is necessary to establish a set of electrical interpretation methods associated with logging curves to realize electrical simulation instead of core measurement. In this paper, an electrical interpretation model of oil-water phase permeability is established based on flowable porosity and pore radius, and the effective seepage dynamic prediction and inversion of oil-water phase in each development stage of each reservoir is realized, which lays a solid foundation for the fine development of the reservoir.

Study of electrical resistivity vertical changes in carbonate oil-bearing reservoirs to determine the depth of free water level

Abstract The purpose of the study is to establish the depth of the free water level (FWL), the zone of oxidized oil, the oil-water transition zone, the zone of maximum and decreasing resistivity. Correct understanding of initial oil saturation zonation in carbonate oil-bearing reservoirs is very important for development planning. The article proposes a method for express assessment of the FWL depth and initial oil saturation zonation in carbonate oil-bearing reservoirs based on electrical resistivity study. The values of electrical resistivity determined from induction logging curves in more than 200 wells were used as initial data. The vertical change in the electrical properties is considered using the resistivity values of oil-bearing reservoirs averaged over all wells. The initial oil saturation zonation was analysed using the graphs of electrical resistivity against depth. As a result of the work done, the following conclusions were made regarding the zonation of initial oil saturation and the possible geological characteristics of carbonate oil-bearing reservoirs: 1) based on the graphs of electrical resistivity against depth it is possible to distinguish 3 zones of initial oil saturation: oil-water transition zone above FWL, zone of maximum resistivity in the middle part of the deposit, and zone of decreasing resistivity in the upper part of the deposit; 2) in general, the initial oil saturation in the reservoir (as a function of the electrical resistivity) upward from the initial FWL depth does not grow exponentially, as in the Leverett function, but linearly in each zone. Proposed express method for initial oil saturation zonation in carbonate reservoirs provides novel information for formation testing and perforation planning. In oil-water transition zone it is recommended to use the gentlest methods of perforation and production intensification to preserve the integrity of the oxidized oil zone, since this zone is water confining and can prevent premature water flooding of the product.

Analysis of Electroencephalographic Signals Using the Root Mean Square (RMS) Fluctuation Function: Applicable Sample Test

Brain signals extracted through brain-computer interface systems (BCI2000- http://www.bci2000.org) allow researchers and computer scientists to cooperate with techniques, mathematical models and statistical inferences that allow the interpretation of a variety of signals provided by people with conditions that significantly affect the ability to move or perform motor activities due to limitations in muscles, bones or nervous system. For this study, we propose a preliminary test with the root mean square (rms) fluctuation function, with EEG data, whose task was the response given to real/imaginary motor stimulus. To validate the model and all the steps up to the configuration of the rms function, we chose the information contained in the EEG of subject S003, available in the public database https://physionet.org/content/eegmmidb/1.0.0/. Considering the distribution of electrodes in the brain (lobes: frontal, parietal, temporal and occipital) and given the data availability conditions (10 - 10 system, EDF format and 160 samples per second), we analyzed 12 of the 64 channels and four stimuli, namely: opening and closing the left or right fist, imagining opening and closing the left or right fist, opening and closing both fists or both feet and imagining opening and closing both fists or both feet. We evaluated their fluctuations individually and the amplitudes of channels 32 and 37 in relation to the others (11, 22, 24, 43, 44, 49, 54, 61, 63 and 64). We observed quantitative similarities when the brain performs the same real/imaginary motor task and that the time of the amplitude changes with the increase of the scale n (time scales). In all experiments (S003_R3, S003_R4, S003_R5, S003_R6), channel 32 x 24, for n > 20 (15 seconds) was smaller than the others, showing that channel 32 (left hemisphere) has the largest fluctuation. From data processing (.EDF) to visualization of F<SUB>DFA</SUB>/∆log curves, we conclude that it is possible to replicate the study for more channels, as well as to investigate other types of activities in the human brain adapted to potential variations (DDP) generated by neurons via signals extracted from the EEG device.

Improving lithofacies prediction in lacustrine shale by combining deep learning and well log curve morphology in Sanzhao Sag, Songliao Basin, China

The accurate identification of shale lithofacies is crucial for characterizing the hydrocarbon potential of lacustrine shale oil reservoirs. Petrophysical logging, serving as an effective tool for acquiring subsurface lithofacies information, provides a convenient and reliable lithofacies identification solution. Deep learning technology, capable of adapting to the nonlinearity and non-stationarity inherent in geological statistics, exhibits unique advantages in conventional reservoir lithofacies prediction. However, the lithofacies of lacustrine shale formations undergo rapid spatial and temporal changes, rendering lithofacies prediction more complex compared to conventional reservoirs. In this study, the lower Qingshankou member in the Sanzhao Sag was selected as the research target, and the Deep Residual Shrinkage Network (DRSN), known for its ability to handle complex nonlinear relationships and mitigate the effects of noisy data through residual connections and shrinkage mechanisms, was employed as a deep learning framework for predicting lithofacies in lacustrine shale formations for the first time. Well logging data, including natural gamma ray (GR), acoustic (AC), deep investigate double lateral resistivity log (RD), shallow investigate double lateral resistivity log (RS), and corrected compensated neutron log (CNC), were used as input features for the model. The results indicate that the DRSN model achieves an accuracy of 76.3% in predicting lithofacies in lacustrine shale formations. However, the DRSN model still exhibits shortcomings in capturing lithofacies change information. To enhance the model's ability to identify lithofacies change interfaces, this study further explicitly introduces Well Logging Curve Morphological Features (WLCM) as additional features and establishes a recognition method combining DRSN with WLCM. The combined DRSN-WLCM model was validated using a separate test dataset, demonstrating an improved accuracy of 85.5%, using the five well logging attributes and the derivative of the AC as inputs. Furthermore, the study reveals the lithofacies spatial distribution characteristics of the lower Qingshankou member in the Sanzhao Sag. This method can be widely applied to lithofacies delineation in lacustrine shale formations and similar stratigraphic units.

Gas Reservoir Identification Based on Nonstationary and Anisotropic FFT-MA Stochastic Modeling

Summary High-resolution reservoir modeling is a crucial technique for the precise identification of gas reservoirs, holding significant importance in guiding natural gas development. However, the nonstationarity and statistical anisotropy of subsurface media present immense challenges to the reliable implementation of high-resolution reservoir modeling. In response to the nonstationarity and anisotropy of complex reservoirs, we propose a novel stochastic modeling method based on fast Fourier transform moving average (FFT-MA). In this method, variational mode decomposition (VMD) is introduced to decompose logging curves into a series of sparse components with specific center frequencies and narrow bandwidths. Subsequently, the autocorrelation functions of each component are computed and synthesized, thereby inferring the nonstationary vertical autocorrelation functions of logging curves. In addition, to characterize the anisotropic and lateral nonstationary features of the reservoir, angle parameters and lateral autocorrelation functions are extracted from seismic records. Lastly, considering the high sensitivity of Lamé constants (λ and μ) and their density-combined counterparts (λρ and μρ) to gas-bearing reservoirs, FFT-MA stochastic modeling is applied to λρ, μρ, and ρ. Gas identification is then performed based on the joint probability distribution extracted from logging data. The proposed method is tested in the sand-shale reservoirs of the Yinggehai Basin, China. The results indicate that the stochastic models for λρ, μρ, and ρ effectively characterize the nonstationary and anisotropic features of complex reservoirs, significantly enhancing the resolution and accuracy of gas identification.

Algorithm for constructing unmeasured logging curves (pseudo-logging) in wells of developed fields

In many cases, the diagrams of some logs in an old well of producing fields are not recorded for some reason or another. Currently, the importance of reassessing the efficiency of such fields is obvious, while the absence of some log curves causes great difficulties. In particular very few studies with acoustic logging (AL) have been carried out in old wells. On the other hand, in 1960-80, among the studies carried out in wells, preference was given to various types of electrical logging (EL), neutron-gamma logging (NGL), and gamma ray logging (GRL). To overcome such difficulties, it is necessary to create diagrams of unmeasured well logs or pseudo-logs. Nowadays, there are methods for constructing pseudolog curves. For example, the method of statistical analysis is widely used. However, in most cases, their implementation is very complex and requires a lot of work. We have developed a new method to construct pseudo-log curves. At first, the propagation velocity of elastic waves in rocks is studied based on seismic survey (SS) and AL data, which are performed presently on the investigation field. Based on this information, objects of interest to the oil industry are selected, their geometric dimensions are determined, the contour of oil and gas saturation is assessed, areas with abnormally high reservoir pressure are studied, the process of accumulation deposition is re-examined, and the preparation of an optimal well network diagram for the effective exploitation of the field is determined. Further, based on the long-ago measured curves of electrical loggings and neutron-gamma logging, we obtain the pseudo-log AL curve in old boreholes and the data SS. The proposed method was tested in 48 wells in the Bahar field (Azerbaijan). The method has shown high efficiency, its reliability is confirmed by independent data. The results of the method help in solving several geological problems: monitoring changes in petrophysical quantities of rocks between wells during development; study of well formations located below the design depth; recalculation of hydrocarbon reserves; determination of the location for drilling new wells, etc

Prediction method for the porosity of tight sandstone constrained by lithofacies and logging resolution

The resource potential of the Upper Triassic Yanchang Formation in the Ordos Basin is considerably large. However, owing to the low porosity and permeability, poor connectivity, and strong heterogeneity of tight sandstone, predicting the porosity of tight sandstone in this formation poses substantial challenges. The lithology of Chang 71 sub-member is dominated by arkose and lithic arkose, wherein the porosities mainly ranged between 3 and 13% and pore types were mainly feldspar dissolution pores. Based on core sample experiments and thin section analysis, combined with conventional logging curves, we proposed a three-porosity weighted average prediction method under lithological control, and a novel method for reading logging curves controlled by logging resolution is developed. We used principal component analysis to optimise the logging curves and reduce the impact of complex coupling relationships between logging curves on lithofacies identification. The stacking algorithm, which is a combination of the random forest and extreme gradient boosting models, was applied to divide the lithofacies into five categories: homogeneous-distributary-channel fine sandstone, heterogeneous-distributary-channel fine sandstone, homogeneous-mouth-bar fine sandstone, heterogeneous-mouth-bar fine sandstone, and shallow lacustrine mudstone. Compared with the results of manual classification based on logs, the accuracy of lithofacies recognition was approximately 94.2%. Additionally, sensitivity analysis of porosity curves was conducted on four types of lithofacies (i.e., except for shallow lacustrine mudstone), and porosity models for each lithofacies were established, providing an effective and objective method for the accurate prediction of porosity. This prediction method comprehensively considers sedimentary factors and incorporates statistics that are fitted using multiple linear regression, which is highly reliable. In the Chang 71 sub-member, the fitting degree between the predicted and core porosities reached 0.912, indicating that this three-porosity weighted average method based on lithofacies constraints is reasonable, reliable, and has stronger adaptability than the those reported previously. The prediction method based on lithofacies control and weight analysis can be applied to other tight sandstones, providing reliable technical support for oil and gas exploration and development.

Evaluation of Fracturing Effect of Tight Reservoirs Based on Deep Learning.

The utilization of hydraulic fracturing technology is indispensable for unlocking the potential of tight oil and gas reservoirs. Understanding and accurately evaluating the impact of fracturing is pivotal in maximizing oil and gas production and optimizing wellbore performance. Currently, evaluation methods based on acoustic logging, such as orthogonal dipole anisotropy and radial tomography imaging, are widely used. However, when the fractures generated by hydraulic fracturing form a network-like pattern, orthogonal dipole anisotropy fails to accurately assess the fracturing effects. Radial tomography imaging can address this issue, but it is challenged by high manpower and time costs. This study aims to develop a more efficient and accurate method for evaluating fracturing effects in tight reservoirs using deep learning techniques. Specifically, the method utilizes dipole array acoustic logging curves recorded before and after fracturing. Manual labeling was conducted by integrating logging data interpretation results. An improved WGAN-GP was employed to generate adversarial samples for data augmentation, and fracturing effect evaluation was implemented using SE-ResNet, ResNet, and DenseNet. The experimental results demonstrated that ResNet with residual connections is more suitable for the dataset in this study, achieving higher accuracy in fracturing effect evaluation. The inclusion of the SE module further enhanced model accuracy by adaptively adjusting the weights of feature map channels, with the highest accuracy reaching 99.75%.

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

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

Missing well logs reconstruction based on cascaded bidirectional long short-term memory network

Well logs are the fundamental data for development and evaluation in the oil and gas field. In the actual exploitation process, missing or incomplete well logs are common due to borehole collapse and instrument failure. In this paper, a cascaded bidirectional long short-term memory network with residual attention is proposed to reconstruct well logs. Firstly, bidirectional long short-term memory (Bi-LSTM) is employed to extract the data features from the forward and backward direction of the missing well logs, considering the bi-directional correlation between the missing data and the context information with depth. Then, the residual structure and the attention mechanism are designed for Bi-LSTM network, named RA-Bi-LSTM, to obtain deeper semantic information of the long-sequence logging curve. Finally, we develop the cascaded RA-Bi-LSTM to solve the multiple logging curves reconstruction problems, where the synthetic logging curve obtained at each RA-Bi-LSTM module is combined with the known logging curves as new input to the next RA-Bi-LSTM module. Hence, unknown missing logging curves can be reconstructed by iterating the cascaded system. Extensive experiments and thorough analysis are validated on four wells in the Sulige gas field in China. Experimental results demonstrate the effectiveness and superiority of the proposed cascade structure on reconstruction accuracy.

Borehole lithology modelling with scarce labels by deep transductive learning

Geophysical logging is a geo-scientific instrument that detects information such as electric, acoustic, and radioactive properties of a well. Its data plays a vital role in interpreting subsurface geology. However, since logging data is an indirect reflection of rocks, it requires the construction of a logging interpretation model in combination with core samples. Obtaining and analysing all core samples in a well is not practical due to their enormous cost, leading to the problem of scarce core sample labels. This problem can be addressed using semi-supervised learning. Existing studies on lithology identification using logging data mostly utilize graph-based semi-supervised learning, which requires known features to establish a graph Laplacian matrix. Therefore, these methods often use logging values at certain depths to construct feature vectors and cannot learn the shape information of logging curves. In this paper, we propose a semi-supervised learning method with feature learning capability based on semi-supervised generative adversarial network (SSGAN) to learn the shape information of logging curves while utilizing unlabelled logging curves. Additionally, considering the problem of insufficient use of labels when dividing a validation set in extremely scarce-label situations, we propose a strategy of weighted averaging of three sub-models, which effectively improves model performance. We verify the effectiveness of our proposed method on five wells and demonstrate the mechanism of semi-supervised learning using adversarial learning through extensive visualization methods.

Control and prediction of bedding-parallel fractures in fine-grained sedimentary rocks: A case from the Permian Lucaogou Formation in Jimusar Sag, Junggar Basin, Western China

The fine-grained sedimentary rocks have numerous bedding-parallel fractures that are essential for the migration, enrichment, and efficient development of oil and gas. However, because of their variety and the complexity of the factors that affect them, their spatial prediction by the industrial community becomes challenging. Based on sample cores, thin sections, and well-logging and seismic data, this study employed a multi-scale data matching approach to quantitatively predict the development of bedding-parallel fractures and investigate their spatial distribution. Bedding-parallel fractures in the Lucaogou Formation in Jimusar Sag frequently occur along preexisting bedding planes and lithological interfaces. Unfilled bedding-parallel fractures inside or near source-rocks exhibit enhanced oil-bearing capacity. They were identified on micro-resistivity scanning images by the presence of regularly continuous black or nearly black sinusoidal curves. Overall, the developmental degree of bedding-parallel fractures was positively related to the brittle mineral and total organic carbon contents and negatively related to single reservoir interval thickness. The maintained porosity of the reservoir matrix contributed to a thorough response to factors affecting the development of bedding-parallel fractures. Here, an effective and objective method was proposed for predicting the development and distribution of bedding-parallel fractures in the fine-grained sedimentary rocks. The method was based on the matched reservoir interval density, reservoir interval density, and matched sweet spot density of bedding-parallel fractures. The prediction method integrated the significant advantages of high vertical resolution from logging curves and strong lateral continuity from seismic data. The average relative prediction error was 8% in the upper sweet spot in the Lucaogou Formation, indicating that the evaluation parameters for bedding-parallel fractures in fine-grained sedimentary rocks were reasonable and reliable and that the proposed prediction method has a stronger adaptability than the previously reported methods. The workflow based on multi-scale matching and stepwise progression can be applied in similar fine-grained sedimentary rocks, providing reliable technological support for the exploration and development of hydrocarbons.