Log Interpretation Research Articles

A comprehensive logging evaluation method for identifying high-quality shale gas reservoirs based on multifractal spectra analysis

Conventional logging interpretation methods qualitatively identify shale reservoirs using shale attribute parameters and interpretation templates. However, improving the identification accuracy of complex shale reservoirs is challenging due to the numerous evaluation parameters and the complexity of model calculations. To quantitatively characterize high-quality shale reservoirs effectively, this study utilizes two wells in the Fuling shale gas field as examples and establishes a comprehensive evaluation method for identifying high-quality shale gas reservoirs utilizing multi-fractal spectral analysis of well logs. First, the conventional well logs are qualitatively analyzed and evaluated via multiple fractals and R/S analysis. Subsequently, a gray relational analysis is employed to combine the production well logging, which reflects dimensionless productivity contributions, with the fractal characteristics of conventional well logs to obtain the corrected weight multifractal spectrum width ∆α' and fractal dimension D’. Comprehensive fractal evaluation indices λ and γ are introduced, forming three categories of productivity evaluation standards for shale gas reservoirs characterized by fractals. Finally, a validation well is employed to demonstrate the effectiveness of the evaluation method. The results indicate that the identification of high-quality shale gas reservoirs based on the above comprehensive fractal evaluation method can reflect the productivity classification level of fractured well sections, simplify the calculation of formation evaluation parameters, and avoid the problem of poor correlation between predicted sweet spot zones and gas production. This approach has wide applicability and value for identifying high-quality reservoir areas in shale gas reservoirs and provides technical support for the effective large-scale development of shale reservoirs.

Application of Two-Dimensional NMR for Quantitative Analysis of Viscosity in Medium–High-Porosity-and-Permeability Sandstones in North China Oilfields

The viscosity of crude oil plays a pivotal role in the exploration and development of oil fields. The predominant reliance on laboratory measurements, which are constrained by manual expertise, represents a significant limitation in terms of efficiency. Two-dimensional nuclear magnetic resonance (NMR) logging offers a number of advantages over traditional methods. It is capable of providing faster measurement rates, as well as insights into fluid properties, which can facilitate timely adjustments in oil and gas development strategies. This study focuses on the loose sandstone reservoirs with high porosity and permeability containing heavy oil in the Huabei oilfield. Two-dimensional nuclear magnetic resonance (NMR) measurements and analyses were conducted on saturated rocks with different-viscosity crude oils and varying oil saturation levels, in both natural and artificial rock samples. This study elucidates the distribution patterns of different-viscosity crude oils within the two-dimensional NMR spectra. Furthermore, the T1 and T2 peak values of the extracted oil signals were employed to establish a model correlating oil viscosity with NMR parameters. Consequently, a criterion for determining oil viscosity based on two-dimensional NMR was formulated, providing a novel approach for estimating oil viscosity. The application of this technique in the BQ well group of the Huabei oilfield region yielded an average relative error of 15% between the actual oil viscosity and the computed results. Furthermore, the consistency between the oil types and the oil discrimination chart confirms the reliability of the method. The final outcomes meet the precision requirements for practical log interpretation and demonstrate the excellent performance of two-dimensional nuclear magnetic resonance (NMR) logging in calculating oil viscosity. The findings of this study have significant implications for subsequent exploration and development endeavors in the research area’s oilfields.

Re-interpreting renewable and non-renewable water resources in the over-pressured Pannonian Basin

With climate change, population growth and the resulting escalating water shortage, humanity is increasingly turning to non-renewable and even fossil groundwater resources, which poses a major challenge to sustainable water management. In this study, 2D basin-scale numerical simulations were carried out on the COMSOL Multiphysics® finite element numerical platform to identify non-renewable water resources in the Central Pannonian Basin (Central Europe, Hungary) based on the lack of hydraulic connection to recharge areas. The concept and boundary conditions (fixed water table configuration at the top, pressure-elevation profiles on the lateral sides, and constant pressure on the bottom) were derived from a previous basin-scale hydraulic data evaluation study, while the hydrostratigraphic subdivision was based on seismic and well log interpretations. As a result, topography-driven groundwater flow systems fed by meteoric water infiltration were separated from a transition zone, which contains non-renewable groundwater resources and covers 85% area of the simulated 110 km long and roughly 1600 m deep cross-section what was previously thought to be fully renewable. Such complex flow pattern and re-interpretation of the renewable and non-renewable groundwater resources can be expected in any terrestrial sedimentary basin with over-pressured flow domains.

An Intelligent Approach to Automated Operating Systems Log Analysis for Enhanced Security

Self-healing systems have become essential in modern computing for ensuring continuous and secure operations while minimising downtime and maintenance costs. These systems autonomously detect, diagnose, and correct anomalies, with effective self-healing relying on accurate interpretation of system logs generated by operating systems (OSs). Manual analysis of these logs in complex environments is often cumbersome, time-consuming, and error-prone, highlighting the need for automated, reliable log analysis methods. Our research introduces an intelligent methodology for creating self-healing systems for multiple OSs, focusing on log classification using CountVectorizer and the Multinomial Naive Bayes algorithm. This approach involves preprocessing OS logs to ensure quality, converting them into a numerical format with CountVectorizer, and then classifying them using the Naive Bayes algorithm. The system classifies multiple OS logs into distinct categories, identifying errors and warnings. We tested our model on logs from four major OSs; Mac, Android, Linux, and Windows; sourced from Zenodo to simulate real-world scenarios. The model’s accuracy, precision, and reliability were evaluated, demonstrating its potential for deployment in practical self-healing systems.

Comprehensive Basin Analysis and Petroleum Potential Evaluation of Ayoluengo Oil Field, Basque-Cantabrian Basin, Spain

This study presents a comprehensive basin analysis and petroleum potential evaluation of the Ayoluengo oil field in the Basque-Cantabrian Basin, Spain. Using an integrated approach combining geological, geophysical, and geochemical data, we analyzed the basin's evolution and petroleum system elements. The dataset included 2D and 3D seismic data, well logs, formation tops, and geophysical maps. Our analysis revealed a complex structural framework characterized by salt diapirism and halokinetic structures. Well log interpretation and sequence stratigraphic analysis identified two genetic sequences with associated systems tracts. Four reservoir units were mapped, with the main structural traps related to salt diapirs. Seismic facies analysis highlighted high-amplitude convergent (CBH) and high- and low-amplitude convergent (CBHL) facies as the most promising for reservoir quality. The study identified one play (Jurassic) and two prospects: Lias Limestone (80% geologic chance of success) and Carniolas Dolomite (85% geologic chance of success). Volumetric calculations estimate significant hydrocarbon potential, with STOIIP (Stock Tank Oil Initially In Place) ranging from 3.4 to 4.2 x 10^16 barrels for the Lias Limestone reservoir and 1.26 to 1.56 x 10^16 barrels for the Carniolas Dolomite reservoir.

Transfer learning for well logging formation evaluation using similarity weights

Machine learning has been widely applied in well logging formation evaluation studies. However, several challenges negatively impacted the generalization capabilities of machine learning models in practical implementations, such as the mismatch of data domain between training and testing datasets, imbalances among sample categories, and inadequate representation of data model. These issues have led to substantial insufficient identification for reservoir and significant deviations in subsequent evaluations. To improve the transferability of machine learning models within limited sample sets, this study proposes a weight transfer learning framework based on the similarity of the labels. The similarity weighting method includes both hard weights and soft weights. By evaluating the similarity between test and training sets of logging data, the similarity results are used to estimate the weights of training samples, thereby optimizing the model learning process. We develop a double experts' network and a bidirectional gated neural network based on hierarchical attention and multi-head attention (BiGRU-MHSA) for well logs reconstruction and lithofacies classification tasks. Oil field data results for the shale strata in the Gulong area of the Songliao Basin of China indicate that the double experts’ network model performs well in curve reconstruction tasks. However, it may not be effective in lithofacies classification tasks, while BiGRU-MHSA performs well in that area. In the study of constructing large-scale well logging processing and formation interpretation models, it is maybe more beneficial by employing different expert models for combined evaluations. In addition, although the improvement is limited, hard or soft weighting methods is better than unweighted (i.e., average-weighted) in significantly different adjacent wells. The code and data are open and available for subsequent studies on other lithofacies layers.

Distribution law of Chang 7 Member tight oil in the western Ordos Basin based on geological, logging and numerical simulation techniques

Abstract Fine characterization of oil plane distribution in highly heterogeneous tight sandstone is a prerequisite for efficient reservoir development. This study systematically evaluated the distribution characteristics of tight oil in the Chang 7 Member of the Western Ordos Basin using a large number of experimental tests, logging interpretation, and 3D modelling methods. The logging interpretation models of shale content, porosity, permeability, and oil saturation were constructed, and the effective reservoir was identified by establishing the intersection identification pattern of reservoir acoustic wave time difference and deep lateral resistivity. The 3D numerical simulation results showed that the tight oil is distributed between injection and production wells. The areas with high tight oil content are mainly distributed along the WE direction, and a series of high remaining oil zones are formed locally. Under the influence of long-term injection and production, a high permeability zone will be formed between wells, which is similar to a high-speed channel and will be flooded quickly, and a banded remaining oil retention zone will be formed around it. For the horizontal well flooding area, the water flooding range of the water injection well is small, and a large amount of remaining oil is enriched between water injection wells. Finally, the classification standard of the remaining oil in the Chang 72 sub-member of the study area is proposed, and then, the strategy of adopting different development and adjustment schemes according to different types of reservoirs is formed.

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%.

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.

Petrophysical Properties Estimation by Using Well Log and Core Data Interpretation for Tertiary Reservoir in Ajeel Oil Field

Understanding the petrophysical properties of reservoirs is paramount for assessing their characteristics and estimating the potential of oil-bearing formations. This study focuses on the tertiary reservoir in the Ajeel oil field, renowned for its heterogeneous carbonate nature and diverse production formations, including Jeribe, Dhiban, and Euphrates. The formation of the tertiary reservoir is evaluated through a comprehensive analysis of petrophysical properties utilizing well log data from well AJ-25 and core analysis data (routine and special analysis). The study reveals significant insights into the petrophysical properties of the formations. The effective porosity values for the Jeribe, Dhiban, and Euphrates formations are determined as 15.41%, 4.65%, and 9.85%, respectively, with lithology predominantly consisting of dolomite rock interspersed with limestone slices and anhydrite nodules. A high Secondary Porosity Index (SPI) indicates fractures. Archie's Parameters, obtained from a Pickett Plot, yield cement coefficient (m=1.8), saturation exponent (n=1.9), and tortuosity factor (a=1.19). Water saturation levels for the Jeribe, Dhiban, and Euphrates formations are 33%, 86%, and 81%, respectively, with porosity and water saturation cutoff values determined as 10% and 60%, respectively. Net to Gross of Jeribe, Dhiban, and Euphrates formation are 0.706, 0.029, and 0.011, respectively. Jeribe Formation is identified as an oil-bearing zone. In contrast, the Dhiban and Euphrates formations are identified as water-bearing zones. To enhance the accuracy of petrophysical assessments, modern well logging techniques, such as the Formation Micro Scanner log (FMI log), are recommended to investigate fractures and their impact on field performance, as traditional logs may not adequately capture these features. Additionally, our comparative analysis highlights superior petrophysical properties of the Euphrates Formation compared to the Jeribe Formation. Therefore, prioritizing drilling and completion activities in the Euphrates Reservoir within the field, contingent upon its positioning relative to the oil-water contact level, is suggested for optimizing reservoir performance.

Development and prospect of acoustic reflection imaging logging processing and interpretation method

Acoustic reflection imaging logging technology can detect and evaluate the development of reflection anomalies, such as fractures, caves and faults, within a range of tens of meters from the wellbore, greatly expanding the application scope of well logging technology. This article reviews the development history of the technology and focuses on introducing key methods, software, and on-site applications of acoustic reflection imaging logging technology. Based on the analyses of major challenges faced by existing technologies, and in conjunction with the practical production requirements of oilfields, the further development directions of acoustic reflection imaging logging are proposed. Following the current approach that utilizes the reflection coefficients, derived from the computation of acoustic slowness and density, to perform seismic inversion constrained by well logging, the next frontier is to directly establish the forward and inverse relationships between the downhole measured reflection waves and the surface seismic reflection waves. It is essential to advance research in imaging of fractures within shale reservoirs, the assessment of hydraulic fracturing effectiveness, the study of geosteering while drilling, and the innovation in instruments of acoustic reflection imaging logging technology.

Analysis and Application of Fluid Components in High-Clay Matrix Shale Oil: A Case Study of Gulong Shale Oil

Fluid components in cores are crucial parameters in evaluating the quality of a shale reservoir in both laboratory analyses and log interpretation. In the Gulong area, shale reservoirs are characterized by a high clay content, with clay spaces hosting both oil and water phases, complicating the occurrence mechanism of fluid components, as a result, traditional research methods are no longer applicable. As an advanced technique, nuclear magnetic resonance (NMR) has been applied in oilfields to determine the specific petrophysical properties of rocks. To more accurately identify the types of fluid components, this study carried out a new, well-designed 2D NMR experiment, rock pyrolysis experiment, and quantitative oil and water detection experiment (QOWDE) to study the Gulong shale. This study measured the 2D NMR map of the original state, saturation state, centrifugal state, and pyrolysis at different temperatures, and conducted mutual verification between the QOWDE and 2D NMR pyrolysis experiments to obtain the distribution of different components of Gulong shale on the 2D NMR map. Based on the experimental results, this study developed a component identification template suitable for the Gulong area and calculated the 2D NMR porosity and saturation from it. This lays a foundation for the analysis and application of fluid components in the Gulong region and provides a new experimental basis and methodological support for porosity and saturation calculations.

Influence of tectonic stress field on oil and gas migration in the Tahe Oilfield carbonate reservoir: Identification of areas favorable for reservoir formation

An understanding of the tectonic stress field distribution in the carbonate reservoirs of the Tahe Oilfield is essential for oil and gas migration and reservoir reconstruction. This study adopts a geological genesis perspective and integrates the unique attributes of the carbonate reservoirs in the Tahe Oilfield to simulate three-dimensional stress fields using the finite element method and Petrel software. The simulation, which takes pore pressure and boundary constraints into consideration, aligns with the findings of acoustic emission tests conducted by previous researchers and validates the model by corroborating the maximum horizontal principal stress direction with imaging logging interpretations. With subsequent utilization of the generalized Coulomb-Mohr shear failure criterion and the Griffith generalized maximum tensile stress criterion, the simulated stress enables the prediction of the total fracture intensity. To complement this analysis, we integrate seismic data and fault formation mechanisms to characterize variations in fracture intensity within distinct regions of the study area. Three key findings emerge from this investigation. First, due to the Hercynian thermal event, X-shaped conjugate faults display a disproportionately high degree of development compared to other primary fractures. Analyzing the mechanisms of formation and development of these X-shaped strike-slip faults reveals discernible differences in deformation characteristics between NW-trending and NE-trending faults, with the former exhibiting a greater propensity for fracture rupture and efficient oil-gas migration. Second, fault intersections are critical and efficient conduits for fluid accumulation and thereby contribute to reservoir formation. Lastly, exclusive of X-shaped conjugate faults, NE-trending faults exhibit the greatest propensity for oil and gas accumulation and migration. Collectively, these findings facilitate the identification of areas favorable for oil and gas migration and provide a crucial mechanical analysis that can inform the exploration and development of the Tahe Oilfield.

Improving the technology for Inflow Profile Evaluation in horizontal Wells by temperature changes Due to calorimetric Mixing

The paper is devoted to the problem of increasing the effectiveness of temperature logging quantitative interpretation for the inflow profile evaluation in horizontal production wells draining heterogeneous reservoirs with low permeability. Such wells are characterized by an extremely uneven distribution of inflow along the length of the wellbore. One of the ways of quantitative interpretation of temperature logging is based on the effect of calorimetric mixing. It’s widely used to quantify the share of local producing intervals in the total flow rate.The low accuracy of interpretation is associated with the lack of reliable information about the temperature of the fluid flowing into the wellbore. The authors propose an estimate of this parameter based on the similarity of the behavior of the temperature field vs time in the near-wellbore zone of a reservoir during periods of stable production and the periods of the well shut-in. This relationship is confirmed by the results of modeling the temperature field of the “well – reservoir” system, taking into account changes in a wide range of reservoir permeability and thermal properties of the reservoir, the geometry of hydraulic fractures in the reservoir, the flowing fluids, as well as the parameters of the well production targets. The logging technology recommended by the authors involves the registration of several temperature profiles along the length of the wellbore at the beginning of the well production with the maximum rate and drawdown and during the well shut-in. Their integrated analysis based on the behavior of the temperature field features in time identified on the basis of modeling makes it possible to evaluate with a high degree of certainty the dynamics of the temperature of the gas-liquid mixture coming from reservoirs to the wellbore during production periods. This provides the required accuracy in the quantitative assessment of the inflow profile from mixing anomalies.The proposed approaches to the interpretation of thermograms are applicable in the analysis of the results of non-stationary temperature logging results in horizontal and vertical wells during the production from heterogeneous reservoirs both through perforation and multi-stage hydraulic fracturing.

Study on quantitative interpretation of uranium spectral gamma-ray logging based on machine learning algorithm

Spectral gamma-ray logging stands out as an efficacious methodology in the exploration of uranium(U). In order to address the challenges associated with low statistical efficacy in spectral analysis and mitigate the impact of spectral drift, machine learning algorithms such as Back Propagation (BP) neural network, Generalized Regression Neural Network (GRNN), and Support Vector Machine (SVM) have been employed for the quantitative interpretation of uranium. A method rooted in machine learning algorithms for energy spectral analysis has been proposed, catering to the analysis of high-speed spectra logging and gamma-ray spectra characterized by spectral drift. Examinations conducted on standard model wells containing uranium revealed that the BP neural network exhibited commendable accuracy in uranium interpretation, achieving quantification accuracy rates of 86.986 % and 93.478 % for low-grade and medium-high-grade uranium ores in the Testing Set, respectively. Notably, marginal variations in the model's quantification errors were observed under diverse logging speeds and spectral drift conditions, underscoring the capacity of gamma-ray spectral quantitative interpretation through machine learning algorithms to effectively surmount the impacts of logging speed and spectral drift. This machine learning-based approach to energy spectral analysis proved instrumental in enhancing traditional logging speeds to 6 m/min, presenting a novel perspective for the quantitative interpretation of uranium spectral gamma-ray logging at heightened logging speeds.

Palaeogeography and tectono‐stratigraphic evolution of the Aptian Ezanga‐Loémé evaporites along the proximal domain of the south Gabon‐Congo‐Cabinda margin

AbstractDuring the Early Cretaceous, massive evaporite accumulations formed in the opening South Atlantic. However, the depositional model of these salts is still poorly constrained at the scale of the West African margin. The present study focuses along the proximal domain of the south Gabon‐Congo‐Cabinda margin and is based on (i) log interpretations of 246 wells crossing undeformed to weakly deformed evaporite intervals and (ii) a structural characterization of the basement. The evaporites show 11 regional evaporite depositional cycles (CI–CXI) bounded by meter‐thick shale beds. The cycles display alternating meter‐scale carnallite‐halite beds that can be correlated over several hundred kilometres, and CVI, CVII, CVIIIa and CX culminate in localized tachyhydrite accumulations. Cross section correlations and isopach maps help to understand the palaeogeographical evolution of each cycle and depositional environments that evolved from relatively deep at the base of cycles, to very shallow at their top. CI formed a mosaic of halite‐prone depocenters deposited in pre‐salt topographic relief. CII and CIII were deposited uniformly over a flattened basin in a highly extended brine pond. From CIV to CVIIIa, the stratigraphic architecture of the salts was shaped by freshwater inflow sourced from the north and possible basement movements. This setting, together with an increased confinement of the proximal domain from the distal one with basin drawdown, favoured the development of depocenters with perennial subaqueous conditions and extreme salinities, in which more than 70 m of tachyhydrite accumulation could locally be preserved. From CVIIIb to CXI, the basin returned to a flat depositional setting without well‐developed depocenters and with increasing subsidence westwards. Marine influx increased starting from CX, allowing the deposition of sulphate beds. The salt section is capped by anhydrite deposits interbedded with clastic and dolomite, before the final marine invasion of the basin. For the first time, this study provides a large‐scale depositional tectonostratigraphic setting of the Aptian salts in the proximal domain of the West African margin. The results are of interest for K‐Mg salts exploration resources in the Aptian and pave the way for further investigation of the salt depositional environment in the distal domain of the margin.

Impacts of sedimentary characteristics and diagenesis on reservoir quality of the 4th member of the Upper Triassic Xujiahe formation in the western Sichuan basin, southwest China

The 4th member of Xujiahe Formation (Xu4) in the western Sichuan Depression is a typical porous tight sandstone reservoir. Finding reservoirs with relatively high porosity controlled by sedimentation and diagenesis is crucial for successful tight gas exploration. In this study, the Xu4 sandstone was studied by diverse methods, including outcrop and core observation, logging data interpretation, thin section and cathodoluminescence examination, scanning electron microscopy analysis, and X-ray diffraction, to determine sedimentary and diagenetic characteristics and to discuss their impact on the sandstone reservoir quality. During the depositional period of Xu4, subaqueous distributary channel sandstone of braided river delta front developed extensively under controls of bidirectional provenance (LASP and SASP), mainly consisting of litharenite and sublitharenite, with low feldspar and high rock fragments volume, respectively. Strong compaction and carbonate cementation have largely destroyed primary porosity, while secondary pores constitute the main pore space in the Xu4 sandstone. The sediment provenance, sedimentary microfacies and A/S determine the clastic composition of sandstone, genetic type and thickness of sand bodies, furthermore affect the subsequent diagenetic variations. The larger the thickness of the sand body indicates a higher-energy depositional environment, coarser grain size, better sorting, and lower shaliness content of the sandstone during deposition period. This results in stronger resistance to compaction of the sandstone, which is conducive to the preservation of primary pores and dissolution of organic acids, resulting in better physical properties of sandstone. Among them, the physical properties of the sandstones of the Xu4s and Xu4x are better than those of the Xu4z; moreover, LASP's sandstone is superior to SASP. It is worth noting that subaqueous distributary channel sandstones with thickness exceeding 13 m in Xu4s and Xu4x from LASP represent favorable areas for high-quality reservoirs. The work can be used as a reference for the exploration and evaluation of similar tight sandstone gas in the world.

A logging evaluation method for shale and calcium-bearing reservoirs based on the effective medium HB resistivity model—a case study on the Sartu Formation and Putaohua Formation West of the Daqing Placanticline

Abstract To solve the problems of low logging interpretation accuracy and fluid identification in shale and calcium-bearing formations in the study area, this paper uses the effective medium theory and electric double layer theory, combined with artificial core technology, to carry out the first quantitative study of the influence of calcareous on rock resistivity, and establishes a conductive model for the evaluation of the oil and gas properties of shale and calcium-bearing formations. First, considering the salt exclusion effect of clay pore water and its dispersion distribution, an improved Berg model was established. Compared with the original Berg model, the improved Berg model achieved a higher interpretation accuracy. Second, artificial cores with different calcium and clay content were designed and prepared using artificial core technology. A quantitative formula describing the relationship between the cementation exponent m and the calcium and clay content was established and combined with the improved Berg model to build an effective medium HB resistivity model suitable for shale and calcium-bearing formations. Finally, the hierarchical decomposition crossplot approach was used to identify the calcareous cemented rock layers, then the reservoir parameters were calculated. The new model is applied to the quantitative evaluation of reservoir logging, and the comparison with the oil test results indicates that the new model established in this paper can be applied to the quantitative evaluation of reservoir logging in this area, and the corresponding evaluation techniques provided a good reference for logging interpretation and evaluation in other corresponding areas.

Multi-factor numerical simulation and response characteristics of two-dimensional nuclear magnetic resonance in digital cores

Two-dimensional nuclear magnetic resonance (2D NMR) logging is an important technology for reservoir evaluation in oil and gas exploration and development, and it has unique advantages in fluid identification. However, in oil shale reservoirs, the mechanisms of 2D NMR logging are complex, and the response characteristics of various factors are unclear. Numerical simulation based on digital core is a significant method to study the influences of multiple factors on 2D NMR. This manuscript introduces the construction method of high-resolution three-dimensional (3D) digital core models, and expounds the principle and workflow of 2D NMR numerical simulation. High-resolution 3D digital cores were constructed based on X-CT, MAPS, and QEMSCAN data of Berea sandstone and Daqing Gulong shale, respectively. Based on 3D digital cores, 2D NMR numerical simulations were carried out on temperature/viscosity, echo spacing, and frequency. Then, 2D NMR mechanisms were analyzed and the response characteristics of multiple factors were summarized. The results show that the T2 and T1 of the water peak have a linear negative correlation with temperature/viscosity, and the T2 and T1 of the oil peak have a linear positive correlation. For Daqing Gulong shale, the T2 of the water peak has a linear positive correlation with echo spacing, while for Berea sandstone, echo spacing has little effect. For Berea sandstone, the T1 of the water peak is linearly positively correlated with frequency. For Daqing Gulong shale, the T1 of the water peak and oil peak are both linearly positively correlated with frequency. The response characteristics of multiple factors could provide a reference and basis for the interpretation and evaluation of 2D NMR logging under complex conditions.

Benchmarking Large Language Models for Log Analysis, Security, and Interpretation

Benchmarking Large Language Models for Log Analysis, Security, and Interpretation