Log Response Research Articles

The Study of Interlayer Identification Based on Well Logging Curve Units

The distribution of interlayers in reservoirs has a significant impact on the flow of reservoir fluids. Accurate identification of interlayer distribution is crucial for analyzing oil–water migration patterns and formulating optimal development strategies. Current research on interlayers mainly focuses on qualitative analysis, particularly regarding sedimentary environments and petrophysical characteristics. However, research on the quantitative analysis of interlayers is relatively scarce, and most identification methods rely on manual interpretation, which introduces human bias. To address this issue, this paper proposes an automatic interlayer identification method based on logging curve units, in P Oilfield. By analyzing the sedimentary and petrophysical characteristics of argillaceous and calcareous interlayers, along with their logging curve responses, multiple logging curves are integrated into a single comprehensive curve. This enables the automatic identification of both single-well and inter-well interlayers, achieving an identification accuracy of up to 90%. This method effectively improves the accuracy and efficiency of interlayer identification and demonstrates high application potential. Analyzing actual data from the P Oilfield, this study reveals the dominant role of interlayers in controlling the distribution of remaining oil in bottom-water reservoirs. It also summarizes three typical remaining oil distribution patterns: basal oil, dome oil, and cap oil. These findings provide practical guidance for subsequent oilfield development and enhanced recovery. This method not only offers an automated solution for interlayer identification in oilfields but also provides scientific evidence for precise decision-making in oilfield development.

Key Technologies for Horizontal Well Development in Deep Tight Sandstone Reservoirs

ABSTRACTDeep tight sandstone oil and gas reservoirs are exerting an increasingly crucial role in the augmentation of fossil energy reserves and the provision of energy. On account of the intricate geological conditions and the deficiency of a comprehensive set of exploration and development engineering technologies as well as supporting processes, the present development of deep tight sandstone oil and gas reservoirs remains in its nascent stage. Through the analysis and generalization of the horizontal well development technology for deep tight sandstone oil and gas reservoirs, a series of technologies have been established, encompassing reservoir geological evaluation and modeling, horizontal well development reservoir engineering validation, horizontal well geological design, and enhanced oil recovery processes. By taking the C 6 reservoir in Ordos basin, China as the research subject, in light of the research outcomes regarding the damage mechanisms and potential damage factors of tight sandstone oil and gas reservoirs, a geological evaluation approach based on the well log response characteristics was constructed, clarifying the porosity and permeability features of the C 6 reservoir, establishing the numerical model of the oil reservoir, and further elaborating the methods for dividing the development layers, selecting the development well pattern, and determining the development well density. The design parameters of horizontal well‐segmented hydraulic fracturing were meticulously optimized, resulting in a minimum cluster spacing of 7 m and a maximum cluster spacing of 20 m. Given the influence of horizontal stress differences, the optimum fracturing density was ascertained to be 16 perforations per meter, and the optimal fracturing fluid volume was identified through simulation to range from 12 to 25 m3/m. The crucial technologies for the development of tight sandstone oil and gas reservoirs in horizontal wells have been clearly identified, offering theoretical direction for the efficient exploitation of deep tight sandstone oil and gas reservoirs.

Reservoir Fluid Identification Based on Bayesian-Optimized SVM Model

Tight sandstone reservoirs are characterized by fine-grained rock particles, a high clay content, and a complex interplay between the electrical properties and gas content. These factors contribute to low-contrast reservoirs, where the logging responses of the gas and water layers are similar, resulting in traditional logging interpretation charts exhibiting a low accuracy in the fluid-type classification. This inadequacy fails to meet the fluid identification needs of the study area’s reservoirs and severely restricts the exploration and development of unconventional oil and gas resources. To address this challenge, this study proposes a fluid identification method based on Bayesian-optimized Support Vector Machine (SVM) to enhance the accuracy and efficiency of the fluid identification in low-contrast reservoirs. Firstly, through a sensitivity analysis of the logging responses, sensitive logging parameters such as the natural gamma, compensated density, compensated neutron, and compensated sonic logs are selected as input data for the model. Subsequently, Bayesian optimization is employed to automatically search for the optimal combination of hyperparameters for the SVM model. Finally, an SVM model is established using the optimized hyperparameters to classify and identify the following four fluid types: water layers, gas layers, gas–water layers, and dry layers. The proposed method is applied to fluid identification in the study area, and comparative experiments are conducted with the K-Nearest Neighbor (KNN), Random Forest (RF), and AdaBoost models. The classification performance of each model is systematically evaluated using metrics such as the accuracy, recall, and F1-score. The experimental results indicate that the SVM model outperforms the other models in fluid identification, achieving an average accuracy of 91.41%. This represents improvements of 16.94%, 4.39%, and 8.30% over the KNN, RF, and AdaBoost models, respectively. These findings validate the superiority of the SVM model for fluid identification in the study area and provide an efficient and feasible solution for fluid identification in tight sandstone reservoirs.

Using Asynchronous Training and Emailed Performance Feedback to Change a Teaching Behavior of Preservice Teachers

The current study used a multiple probe design to determine the effects of an intervention package (asynchronous training plus emailed specific performance feedback) on the rate of opportunities to respond (OTR) of three preservice elementary teachers providing small group mathematics instruction. The study utilized Swivl to record and upload lessons, which were viewed and coded to determine the rate per minute of OTR. Results show a functional relation between the intervention package and increased rates of OTR across all participants. Calculations using Nonoverlap of All Pairs (NAP) and Log Response Ratio (LRRi) suggest strong effects on the rate of OTR. Social validity was determined by semi-structured interviews, during which participants noted the intervention package to be effective at changing their behavior and simple to implement. Limitations and future research directions are addressed.

Terminal Fan Deposition and Diagenetic Control in the Lower Paleogene of the Shahejie Formation, Bonan Sag, Bohai Basin, China: Insights into Reservoir Quality

In the Bonan area, the lower fourth member of the Shahejie Formation (Es4x) is buried beneath a sedimentary pile ranging from 2500 to 5000 m. Understanding the impact of diagenetic alterations on these deeply buried reservoirs is crucial for effective hydrocarbon exploration and production. This study employs a terminal fan sedimentation model, encompassing depositional environments such as feeder channels, distributary channels, floodplains, and basinal zones, to provide insights into the spatial distribution of reservoir properties and their influence on the localization of optimal reservoirs within the sag. The analysis integrates diagenetic facies with well log responses, subsurface porosity trends, and permeability variations across the formation. The petrographic analysis indicates that the sandstone is composed primarily of litharenite, feldspathic litharenite, lithic arkose, and minor amounts of arkose. The dominant clay cement is illite, accompanied by mixed-layer smectite/illite, chlorite, and kaolinite. Thin section observations reveal secondary porosity formed through the dissolution of quartz grains, volcanic rock fragments, and feldspar, along with their associated cements. These sandstones exhibit relatively good sorting, with average porosity and air permeability values of 14.01% and 12.73 mD, respectively. Diagenetic alterations are categorized into three processes: porosity destruction, preservation, and generation. Key diagenetic mechanisms include compaction, cementation, replacement, and dissolution, with compaction exerting the most significant control on reservoir porosity reduction. Statistical analysis indicates that the average porosity loss due to compaction is approximately 13.3%, accounting for about 38% of the original porosity. The detrital rock cement predominantly comprises quartz (42%), feldspar (32%), clay minerals (14%), and carbonate (12%). Under the prevailing depositional conditions, porosity is enhanced by dissolution and fracturing, while late-stage diagenetic cementation by clay and carbonate minerals—excluding chlorite—adversely affects reservoir quality. Consequently, the distributary zone is identified as the primary target for exploration.

Experimental Analysis of Resistivity Logging Response of Marine Gas Hydrate Reservoirs

Experimental Analysis of Resistivity Logging Response of Marine Gas Hydrate Reservoirs

Non-native species have higher consumption rates than their native counterparts.

Non-native species can be major drivers of ecosystem alteration, especially through changes in trophic interactions. Successful non-native species have been predicted to have greater resource use efficiency relative to trophically analogous native species (the Resource Consumption Hypothesis), but rigorous evidence remains equivocal. Here, we tested this proposition quantitatively in a global meta-analysis of comparative functional response studies. We calculated the log response ratio of paired non-native and native species functional responses, using attack rate and maximum consumption rate parameters as response variables. Explanatory variables were consumer taxonomic group and functional feeding group, habitat, native assemblage latitude, and non-native species taxonomic distinctiveness. Maximum consumption rates for non-native species were 70% higher, on average, than those of their native counterparts; attack rates also tended to be higher, but not significantly so. The magnitude of maximum consumption rate effect sizes varied with consumer taxonomic group and functional feeding group, being highest in favour of non-natives for molluscs and herbivores. Consumption rate differences between non-native and native species tended to be greater for freshwater taxa, perhaps reflecting sensitivity of insular freshwater food webs to novel consumers; this pattern needs to be explored further as additional data are obtained from terrestrial and marine ecosystems. In general, our results support the Resource Consumption Hypothesis, which can partly explain how successful non-native species can reduce native resource populations and restructure food webs.

Advanced Machine Learning for Low-Data Porosity and Permeability Prediction in Tight Sandstones

Accurate prediction of porosity and permeability is crucial for understanding subsurface fluids. Traditional physical methodologies, however, are both costly and time-consuming. Moreover, existing machine learning predictive methods require a substantial number of samples, leading to performance bottlenecks. In response to the acute scarcity of core data and weak logging responses in actual working areas, we propose a novel machine learning algorithm based on the REaLTabFormer-voting in Extra Trees, XGBoost, and Random Forest (RTF-vEXR) model, for porosity and permeability prediction using well logging data. RTF-vEXR consists of two primary components: First, the REaLTabFormer data generation model is introduced, which captures the intrinsic correlations between logging parameters and target parameters (porosity and permeability), thereby enhancing the quality of core data. Second, we employ a vEXR-based ensemble regression model, which exhibits robustness and fitting ability, to achieve accurate prediction of porosity and permeability in tight sandstone reservoirs. In practice, we implement the proposed RTF-vEXR model in the Sulige Gas Field for porosity and permeability prediction. Extensive experimental results indicate that, compared to other baseline methods, our proposed RTF-vEXR model provides the best fit for core porosity and permeability, achieving the highest R-squared value, as well as the lowest mean absolute error and root mean square error, demonstrating the feasibility and effectiveness of the method for predicting porosity and permeability. Furthermore, ablation experiments indicate that integrating the RTF module can significantly enhance the model’s performance in dealing with low-data scenarios.

Response and multi-parameter determination of microresistivity scanning imaging logging while drilling in oil-based drilling fluid using numerical modeling and machine learning

Response and multi-parameter determination of microresistivity scanning imaging logging while drilling in oil-based drilling fluid using numerical modeling and machine learning

MacBehaviour: An R package for behavioural experimentation on large language models

The study of large language models (LLMs) and LLM-powered chatbots has gained significant attention in recent years, with researchers treating LLMs as participants in psychological experiments. To facilitate this research, we developed an R package called “MacBehaviour “ (https://github.com/xufengduan/MacBehaviour), which interacts with over 100 LLMs, including OpenAI's GPT family, the Claude family, Gemini, Llama family, and other open-weight models. The package streamlines the processes of LLM behavioural experimentation by providing a comprehensive set of functions for experiment design, stimuli presentation, model behaviour manipulation, and logging responses and token probabilities. With a few lines of code, researchers can seamlessly set up and conduct psychological experiments, making LLM behaviour studies highly accessible. To validate the utility and effectiveness of “MacBehaviour,“ we conducted three experiments on GPT-3.5 Turbo, Llama-2-7b-chat-hf, and Vicuna-1.5-13b, replicating the sound-gender association in LLMs. The results consistently demonstrated that these LLMs exhibit human-like tendencies to infer gender from novel personal names based on their phonology, as previously shown by Cai et al. (2024). In conclusion, “MacBehaviour” is a user-friendly R package that simplifies and standardises the experimental process for machine behaviour studies, offering a valuable tool for researchers in this field.

Investigation of Coal Structure and Its Differential Pore–Fracture Response Mechanisms in the Changning Block

The deep coal seams in the southern Sichuan region contain abundant coalbed methane resources. Determining the characteristics and distribution patterns of coal structures in this study area, and analyzing their impact on pore and fracture structures within coal reservoirs, holds substantial theoretical and practical significance for advancing coal structure characterization methods and the efficient development of deep coalbed methane resources. This paper quantitatively characterizes coal structures through coal core observations utilizing the Geological Strength Index (GSI) and integrates logging responses from different coal structures to develop a quantitative coal structure characterization model based on logging curves. This model predicts the spatial distribution of coal structures, while nitrogen adsorption data are used to analyze the development of pores and fractures in different coal structures, providing a quantitative theoretical basis for accurately characterizing deep coal seam features. Results indicate that density, gamma, acoustic, and caliper logging are particularly sensitive to coal structure variations and that performing multiple linear regression on logging data significantly enhances the accuracy of coal structure identification. According to the model proposed in this paper, primary-fragmented structures dominate the main coal seams in the study area, followed by fragmented structures. Micropores and small pores predominantly contribute to the volume and specific surface area of the coal samples, with both pore volume and specific surface area increasing alongside the degree of coal fragmentation. Additionally, the fragmentation of coal structures generates more micropores, enhancing pore volume and suggesting that tectonic coal has a greater adsorption capacity. This study combines theoretical analysis with experimental findings to construct a coal structure characterization model for deep coal seams, refining the limitations of logging techniques in accurately representing deep coal structures. This research provides theoretical and practical value for coal seam drilling, fracturing, and reservoir evaluation in the southern Sichuan region.

Prediction of coal structures and its gas-bearing properties based on geophysical logging parameters: A case study in Anze block, China

Coal structures are widely regarded as a critical influencing factor for the dynamic behaviors of CH4 migration in coalbed methane (CBM) reservoir. In this paper, geophysical logging data were analyzed to explore the logging response characteristics of coal structures, and their application on identification of coal structures by using the machine learning methods. Meanwhile, the correlations between coal structures and gas-bearing properties were revealed. The results show that with the increase in coal deformation intensities, acoustic transit time, caliper logging, compensated neutron, and natural gamma values positively increase and that for density logging and lateral resistivity show a negative correlation. The multi-logging parameter identification models of coal structures were constructed by using random forest algorithm, radial basis function neural network, and long short-term memory neural network, with their accuracy reaching to 96.67%, 93.33%, and 91.67%, respectively. Based on the identification results of RFA model, the highest distribution percentages of cataclastic coal are 50.2%, which is controlled by tectonic activities and buried depth. The origins of gases are mainly thermogenic gases whose average value of δ13C(CH4) is −37.51‰. The gas content in granulated coal is smaller than 12 cm3/g, but it is higher than 15 cm3/g in cataclastic coal, resulting the higher gas saturation of cataclastic coal. The average extension length of artificial fractures in cataclastic coals is nearly two times as long as in granulated coals. It is suggested that cataclastic coal zone is the favorable area for CBM development.

Identification of Water-Flooded Layers and Evaluation of Flooded Levels in Fractured Tight Sand Oil Reservoir

Owing to the heterogeneity of tight reservoirs, the development of fractures, and the unreasonable water injection development method, the logging response characteristics of water-flooded layers in fractured tight sandstone reservoirs in the H oilfield in the Junggar Basin are complex, making it difficult to identify and evaluate them quantitatively. This has severely restricted the treatment of water-flooded layers and the optimization of development methods. This study calibrates logging data with dynamic production data and experimental analysis data. Based on clarifying the logging response characteristics of the water-flooded layers, these layers were qualitatively identified through radial resistivity differences and original resistivity recovery methods. By constructing models for the water cut and extraction index, which reflect water flooding characteristics, quantitative evaluations of water flooding levels can be conducted on the basis of consistency principles. Furthermore, the impacts of structural location, sedimentary environment, and fracture development on the water-flooded layers were analyzed. The results suggest that the radial resistivity difference method, along with the original resistivity recovery approach, can effectively identify water-flooded layers. By integrating the water cut, the extraction index, and the radial resistivity difference chart, the influence of reservoir heterogeneity is mitigated, resulting in improved quantitative characterization accuracy and efficiency for water-flooded layers in fractured tight sandstone reservoirs. There are two primary mechanisms for the formation of water-flooded layers in the H oilfield: matrix pore infiltration and differential water drive through fractures. The lower parts of the structure and areas with developed channel sedimentary microfacies are more susceptible to the formation of water-flooded layers, and the remaining oil potential in the matrix pores of the fracture development zone in the northeast of the oilfield is objective.

A more holistic view of the logarithmic dose-response curve offers greater insights into insulin responses

The stimulus-response curve is usually modeled by the Hill function due to its simplicity and clear molecular mechanisms (Michaelis-Menten type of kinetics). Unfortunately, the mechanisms do not explain why the stimulus is ubiquitously measured by logarithmic dose rather than the dose itself and why the log(dose)-response curve possesses such fine properties as symmetry and wide adjustability. Here, the dose response is considered from a holistic perspective spanning multiple biological levels from molecules to the whole organism, which reveals that an appropriate model for log(dose) response is the cumulative normal distribution (CND) function, which had only statistical implication previously but now possess mechanistic-statistical duality. The present CND model establishes a connection between single-cell all-or-none responses and the graded response at the tissue/organism level, reveals the raison d'être of the logarithmic transformation, explains why log(dose)-response curve possesses many fine properties, and reveals new mechanisms of tissue/organism dose-response, including homogeneity-induced sensitivity. It also provides new insights into vital biological processes, such as the insulin dose response.

Evaluating the correspondence between expert visual analysis and quantitative methods.

Visual analysis is the primary methodology used to determine treatment effects from graphed single-case design data. Previous studies have demonstrated mixed findings related to interrater agreement between both expert and novice visual analysts, which represents a critical limitation of visual analysis and supports calls for also presenting statistical analyses (i.e., measures of effect size). However, few single-case design studies include results of both visual and quantitative analyses for the same set of data. The present study investigated whether blind review of single-case graphs constructed using up-to-date recommendations by experts in visual analysis would demonstrate adequate interrater agreement and have correspondence with an effect size metric, log response ratio. Eleven experts (i.e., professors in school psychology and special education with visual analysis experience) analyzed 26 multiple-baseline graphs evaluating implementation planning, a fidelity support, on educators' implementation and student outcomes, presented in a standardized format without indication of the variable being measured. Results suggest that there was strong correspondence between raters in their judgments of the presence or absence of treatment effects and meaningfulness of effects (particularly for graphs of adherence and quality). Additionally, a quadratic relationship was observed between aggregate results of expert visual analysis and effect size statistics. Implications for future research and limitations are discussed. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Application of FLAIR technology in evaluation of complex fluids—Taking M structure of Huizhou sag as an example

Abstract Due to the complexity of reservoir fluid types and the difficulty in identifying hydrocarbon abundance in M structure of the Huizhou sag, the conventional gas logging methods are affected by instrument accuracy, resulting in indistrument difference in gas logging response characteristics. Consequently, the traditional evaluation methoda are inadequate.To address these challenges, fluid logging & analysis in real time technology(FLAIR) is applied. This technology quantifies various gas components by detecting the ion mass, offering advantages such as precise detection and wide measurement range.In this paper,the data of FLAIR technology was analyzed. Based on the differences in hydrocarbon components of various fluids, fluid identification charts were developed using the hydrocarbon component ratio, humidity ratio (WH) and equilibrium ratio (BH) parameters, which could effectively distinguish complex fluids such as gas, condensate, oil and volatile oil formations.The oil index and gas index were selected, and the gas-oil ratio index was formulated. A relationship model between this parameter and the gas-oil ratio data, measured through cable sampling and formation testing was established, enabling the prediction of gas-oil ratio data during drilling. By correcting gas data to reduce the influence of factors such as drilling time, displacement volume and bit size through gas data correction, the evaluation chart of oil and gas abundance, based on the anomaly ratio of gas measurement can adress the evaluation challenges of buried hill reservoir. The aforementioned method provides robust technical support for reservoir fluid identification and evaluation in M structure of Huizhou sag, and has significant application value in complex reservoirs and complex fluid conditions.

Efficacy, non-target impacts, and costs of mechanical control options against a bioturbator in bivalve aquaculture

Bivalve aquaculture benefits from non-chemical, operational-scale pest management tools to sustain or increase production. Mechanical methods to control a bioturbating pest (burrowing shrimp Neotrypaea californiensis) have been tested in Washington State (USA), providing a roadmap for other regions affected by native or widespread pests. These shrimp smother and bury ground-cultured oysters on tidal flats even when the shrimp are at low densities. Therefore, control methods with high efficacy are required, also considering non-target effects and costs to implement. From 2002 to 2023, 55 mechanical control trials were carried out, plus two shell-addition trials from earlier research. Methods included: 1) surface barriers, 2) shrimp removal, 3) sediment disruption, and 4) physical conditions intended to cause direct mortality (e.g. electricity, heat). Data were compiled through a meta-analysis framework, in which effect size was calculated as the log response ratio of treated relative to reference plots. Most surface barriers were not effective because they were penetrated by shrimp or insufficiently anchored; an exception was application of five inches (12.7 cm) of gravel. Shrimp removal was effective with a water-jet technique developed to collect shrimp for bait at low tide, but efforts to deploy multiple jets with a towed mechanized device were unsuccessful. Sediment disruption through surface compaction was the most common farm-scale approach but insufficiently reduced shrimp densities, whereas consolidating sediment with vibration to 1 m depth had high efficacy but has been applied only in small plots. Effect sizes were not available for any field trials of direct mortality methods, but energy required to kill shrimp was calculated from laboratory studies (1–50 kW-hr m−3 in water, and higher energy required in saturated sediment). Across all field methods, efficacy improved with effort (person-hours per area). Sediment showed reduced penetrability and increased muddiness following treatment. Non-target effects on infauna included both positive and negative effect sizes, consistent with a community change following a reduction in shrimp density. In the six studies measuring epibenthic species, no overall positive response to control of shrimp occurred, even though the reason for control is to protect surface-dwelling species from bioturbation by shrimp. This outcome illustrates the importance of pairing efficacy in terms of reduction of shrimp and improvement of farming. The vertical position of N. californiensis below nearly a meter of water-saturated sediment, along with an innate tolerance to pressure and anoxia, has placed them out of reach of most attempted mechanical methods. Although progress has been made in recent years, mechanical control options remain limited that can be carried out without permits and that economically reduce shrimp to densities compatible with benthic shellfish aquaculture at a farm scale.

Improved Fracture Permeability Evaluation Model for Granite Reservoirs in Marine Environments: A Case Study from the South China Sea

Permeability is a crucial parameter in the exploration and development of oil and gas reservoirs, particularly in unconventional ones, where fractures significantly influence storage capacity and fluid flow. This study investigates the fracture permeability of granite reservoirs in the South China Sea, introducing an enhanced evaluation model for planar fracture permeability based on Darcy’s law and Poiseuille’s law. The model incorporates factors such as fracture heterogeneity, tortuosity, angle, and aperture to improve permeability assessments. Building on a single-fracture model, this research integrates mass transfer equations and trigonometric functions to assess intersecting fractures’ permeability. Numerical simulations explore how tortuosity, angle, and aperture affect individual fracture permeability and the influence of relative positioning in intersecting fractures. The model makes key assumptions, including minimal consideration of horizontal stress and the assumption of unidirectional laminar flow in cross-fractures. Granite outcrop samples were systematically collected, followed by full-diameter core drilling. A range of planar models with varying fracture apertures were designed, and permeability measurements were conducted using the AU-TOSCAN-II multifunctional core scanner with a steady-state gas injection method. The results showed consistency between the improved model and experimental findings regarding the effects of fracture aperture and angle on permeability, confirming the model’s accuracy in reflecting the fractures’ influence on reservoir flow capacity. For intersecting fractures, a comparative analysis of core X-ray computed tomography (X-CT) scanning results and experimental outcomes highlighted discrepancies between actual permeability measurements and theoretical simulations based on tortuosity and aperture variations. Limitations exist, particularly for cross-fractures, where quantifying complexity is challenging, leading to potential discrepancies between simulation and experimental results. Further comparisons between core experiments and logging responses are necessary for model refinement. In response to the challenges associated with evaluating absolute permeability in fractured reservoirs, this study presents a novel theoretical assessment model that considers both single and intersecting fractures. The model’s validity is demonstrated through actual core experiments, confirming the effectiveness of the single-fracture model while highlighting the need for further refinement of the dual-fracture model. The findings provide scientific support for the exploration and development of granite reservoirs in the South China Sea and establish a foundation for permeability predictions in other complex fractured reservoir systems, thereby advancing the field of fracture permeability assessment.

Rapid identification of an effective bauxite gas reservoir by principal component analysis.

In recent years, industrial gas flow has been obtained from the bauxite gas reservoir in the southwestern Ordos Basin, which has made the identification of aluminium-bearing rock reservoirs a popular topic. To accelerate the exploration and development of this type of gas reservoir, major element testing, rock thin section identification and principal component analysis (PCA) were conducted, and a method for rapid and accurate identification of bauxite reservoirs via conventional logging was established. The test results clearly revealed the vertical stratification of major elements and three lithologies in the aluminium (Al)-bearing rock series in the study area. The log response characteristics of effective gas reservoirs were summarized, providing a basis for subsequent research on identifying effective bauxite reservoirs via mathematical dimensionality reduction of logging curves. The porosity comparison of strata with different lithologies suggests that dissolution pores are more developed in Al-rich layers, providing insight for identifying effective reservoirs by Al2O3 content. On the basis of the above findings, a lithological identification chart of Al-bearing rock series was established via principal component analysis (PCA), and an effective bauxite reservoir logging identification model based on Al2O3 content prediction was developed. The results show that using the dimensionality reduction method for principal component analysis of logging curves with overlapping information can avoid model distortion caused by multicollinearity. The research results can be used to identify bauxite reservoirs quickly and accurately without other test data.

Improvement of Sonic Logging Porosity Calculation Method in South Sulige Block

Abstract South Sulige Block is a lithologic gas reservoir with low porosity and permeability, low pressure and low abundance, of which the heterogeneity is very high and pore structure is complex. Due to the influence of lithology and pore structure, the skeleton value is uncertain and it is hard to give a fixed value. It is difficult to obtain the true porosity of formation accurately by using the traditional method of calculating porosity by Wyllie formula in sonic logging. In this paper, according to the principle of sonic logging and rock response characteristics, combined with core analysis data of this area and adjacent areas, using the acoustic time and core analysis porosity regression formula to calculate porosity first, and then, according to different shale content, the corresponding shale correction is carried out for heavy shale content intervals on the basis of core regression formula, which eliminates the porosity calculation error caused by the influence of shale content. Practical application shows that the porosity calculated by this method is in good agreement with the results of core analysis, and can reflect the reservoir characteristics and production capacity of the formation more truly.