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The aim of this paper is to investigate the re-use of research data deposited in digital data archive in the social sciences. The study examines the quantity, type, and purpose of data downloads by analyzing enriched user log data collected from Swiss data archive. The findings show that quantitative datasets are downloaded increasingly from the digital archive and that downloads focus heavily on a small share of the datasets. The most frequently downloaded datasets are survey datasets collected by research organizations offering possibilities for longitudinal studies. Users typically download only one dataset, but a group of heavy downloaders form a remarkable share of all downloads. The main user group downloading data from the archive are students who use the data in their studies. Furthermore, datasets downloaded for research purposes often, but not always, serve to be used in scholarly publications. Enriched log data from data archives offer an interesting macro level perspective on the use and users of the services and help understanding the increasing role of repositories in the social sciences. The study provides insights into the potential of collecting and using log data for studying and evaluating data archive use. [ABSTRACT FROM AUTHOR]

Since the beginning of the 20th century, many studies have focused on the possibility of considering the sky as a body characterized by an apparent temperature, and several correlations to quantify the apparent sky temperature have been proposed. However, the different models were obtained for specific meteorological conditions and through measurements at specific sites. The available models do not cover all locations in the world, although the evaluation of the sky temperature is fundamental for estimating the net radiative heat transfer between surfaces and the sky. Here, experimental data logged from a regional micrometeorological network (in Italy, within the Lazio region) were processed and used to identify an empirical model for the estimation of the sky temperature in the Mediterranean area. Data relating to atmospheric infrared radiation were used to compute the sky temperature, aiming at identifying a direct correlation with the ambient temperature. Climatic data acquired during 2022 were processed. The proposed correlations were compared with other models available in the literature, including the standard ISO 13790. This study proposes an annual-based direct correlation in its initial phase, demonstrating a superior fit to the measured data compared to well-known direct empirical models from the literature. Subsequently, quarterly-based correlations are introduced further in a secondary phase of the work to improve the model's adaptation to experimental observations. The results reveal that quarterly-based correlations improve goodness-of-fit indexes compared to annual-based and well-known direct empirical correlations. Finally, a detached building was modeled via a dynamic code to highlight the influence of different correlations on annual energy needs. [ABSTRACT FROM AUTHOR]

First-person captioning is significant because it provides veracious descriptions of egocentric scenes in a unique perspective. Also, there is a need to caption the scene, a.k.a. life-logging, for patients, travellers, and emergency responders in an egocentric narrative. Ego-captioning is indeed non-trivial since (1) Ego-images can be noisy due to motion and angles; (2) Describing a scene in a first-person narrative involves drastically different semantics; (3) Empirical implications have to be made on top of visual appearance because the cameraperson is often outside the field of view. We note we humans make good sense out of casual footage thanks to our contextual awareness in judging when and where the event unfolds, and whom the cameraperson is interacting with. This inspires the infusion of such "contexts" for situation-aware captioning. We create EgoCap which contains 2.1K ego-images, over 10K ego-captions, and 6.3K contextual labels, to close the gap of lacking ego-captioning datasets. We propose EgoFormer , a dual-encoder transformer-based network which fuses both contextual and visual features. The context encoder is pre-trained on ImageNet before fine tuning with context classification tasks. Similar to visual attention, we exploit stacked multi-head attention layers in the captioning decoder to reinforce attention to the context features. The EgoFormer has realized state-of-the-art performance on EgoCap achieving a CIDEr score of 125.52. The EgoCap dataset and EgoFormer are publicly available at https://github.com/zdai257/EgoCap-EgoFormer. • First quantitative study of image captioning in an egocentric perspective. • Introduction of transformer-based context fusion architecture. • EgoCap, a sizable first-person image caption dataset, is released. [ABSTRACT FROM AUTHOR]

Background: Decision-making in healthcare is increasingly complex; notably in hospital environments where the information density is high, e.g., emergency departments, oncology departments, and psychiatry departments. This study aims to discover decisions from logged data to improve the decision-making process. Methods: The Design Science Research Methodology (DSRM) was chosen to design an artifact (algorithm) for the discovery and visualization of decisions. The DSRM's different activities are explained, from the definition of the problem to the evaluation of the artifact. During the design and development activities, the algorithm itself is created. During the demonstration and evaluation activities, the algorithm was tested with an authentic synthetic dataset. Results: The results show the design and simulation of an algorithm for the discovery and visualization of decisions. A fuzzy classifier algorithm was adapted for (1) discovering decisions from a decision log and (2) visualizing the decisions using the Decision Model and Notation standard. Conclusions: In this paper, we show that decisions can be discovered from a decision log and visualized for the improvement of the decision-making process of healthcare professionals or to support the periodic evaluation of protocols and guidelines. [ABSTRACT FROM AUTHOR]

Deep-water currents are geographically widespread and represent important tight-oil and -gas reservoirs. However, identifying deep-water traction current deposits is challenging work. The main objectives of this research were to identify a new type of reservoir deposited in deep-water traction currents. Based on high-quality 3D seismic data and drilling data (logging data and lithology), the sedimentary characteristics of the MKT Formation of the upper Carboniferous Moscow Stage, southeastern Pre-Caspian Basin, were determined. The MKT Formation of the upper Carboniferous Moscow Stage is mainly composed of mudstone and some thin-bedded siltstone. This formation contains a series of "reversal foresets" dipping west (early paleo–high provenance during the depositional stage). Based on the seismic data and drilling logging data, lithology, paleo-geographic position, seismic facies, and form and scale, deep-water traction current deposits were identified in the M block. The discovery of deep-water traction current deposits in the M block not only provides a precious example for research on Paleozoic deep-water traction current deposits, and enriches our knowledge of basin sedimentary types, but also proves that the M block had complex fluid features and unveils a new domain for petroleum exploration in the basin. [ABSTRACT FROM AUTHOR]

There are several productive petroleum fields in the North Western Desert (WD) of Egypt, which received extensive investigations regarding their petroleum potential. However, a few studies tackled the Matruh Oil Field, which contains the oil prolific Early Cretaceous Alam El-Bueib Formation (AEB Fm) reservoir. The reservoir intervals of the AEB Fm show substantial lithological variations across the basin. Therefore, it is necessary to analyze the vertical and lateral distributions in terms of their lithological and petrophysical properties. To achieve this objective, wireline logs of four wells and 20-2D seismic lines were used to construct a depth-structure contour map for the studied part of the field. This map was used to build the field's structure model and to identify the fault patterns in the basin through several seismic lines. Analyses of well logs data and lithology were used to estimate the petrophysical properties of AEB sandstone units AEB-1, AEB-3A, AEB-3C, and AEB-6. Results show that the AEB-6 Unit is the most promising hydrocarbon-bearing unit. It has a net pay of 20–160 feet, a shale volume of 5–20%, an effective porosity of 14–20%, and a hydrocarbon saturation of 70–88%. The structure-depth maps indicate a number of normal faults with two principal NE-SW and NW–SE trends, which probably act as structural traps in the Matruh Oil Field. The constructed structure-depth maps and calculated petrophysical parameters were used to build a three-dimensional reservoir model. A blind well was used to validate the accuracy and reliability of the facies, porosity, and saturation models for the AEB Fm units, ensuring a good match between log-derived data and built models. The AEB Fm shows regional heterogeneous variations in its petrophysical characteristics. It exhibits unconventional reservoir characteristics in a N–S direction and conventional reservoir characteristics in an E–W direction. This observed heterogeneity shows the need to carry out further investigations to comprehensively assess the hydrocarbon potential of AEB Fm in different areas of the Matruh Basin. [ABSTRACT FROM AUTHOR]

Many methods have been developed to detect and predict the fracture properties of fractured rocks. The standard data sources for fracture evaluations are image logs and core samples. However, many wells do not have these data, especially for old wells. Furthermore, operating both methods can be costly, and, sometimes, the data gathered are of bad quality. Therefore, previous research attempted to evaluate fractures indirectly using the widely available conventional well-logs. Sedimentary rocks are widespread and have been studied in the literature. However, fractured reservoirs, like igneous and metamorphic rock bodies, may also be vital since they provide fluid migration pathways and can store some hydrocarbons. Hence, two fractured metamorphic rock bodies are studied in this study to evaluate any difference in fracture responses on well-log properties. Also, a quick and reliable prediction method is studied to predict fracture density (FD) in the case of the unavailability of image logs and core samples. Gene expression programming (GEP) was chosen for this study to predict FD, and ten conventional well-log data were used as input variables. The model produced by GEP was good, with R2 values at least above 0.84 for all studied wells, and the model was then applied to wells without image logs. Both selected metamorphic rocks showed similar results in which the significant parameters to predict FD were the spectral gamma ray, resistivity, and porosity logs. This study also proposed a validation method to ensure that the FD value predictions were consistent using discriminant function analysis. In conclusion, the GEP method is reliable and could be used for FD predictions for basement metamorphic rocks. [ABSTRACT FROM AUTHOR]

In petroleum engineering, real‐time lithology identification is very important for reservoir evaluation, drilling decisions and petroleum geological exploration. A lithology identification method while drilling based on machine learning and mud logging data is studied in this paper. This method can effectively utilize downhole parameters collected in realtime during drilling, to identify lithology in real‐time and provide a reference for optimization of drilling parameters. Given the imbalance of lithology samples, the synthetic minority over‐sampling technique (SMOTE) and Tomek link were used to balance the sample number of five lithologies. Meanwhile, this paper introduces Tent map, random opposition‐based learning and dynamic perceived probability to the original crow search algorithm (CSA), and establishes an improved crow search algorithm (ICSA). In this paper, ICSA is used to optimize the hyperparameter combination of random forest (RF), extremely random trees (ET), extreme gradient boosting (XGB), and light gradient boosting machine (LGBM) models. In addition, this study combines the recognition advantages of the four models. The accuracy of lithology identification by the weighted average probability model reaches 0.877. The study of this paper realizes high‐precision real‐time lithology identification method, which can provide lithology reference for the drilling process. [ABSTRACT FROM AUTHOR]

Oilfield production is controlled by a wide range of geological and engineering parameters, many of which are at least partially interrelated. This paper uses multivariate statistical methods (principal component analysis, regression analysis and analysis of variance) to determine how these parameters are related, and which of them are most significant in controlling and predicting oilfield performance. The analysis is based on a database of publicly available oilfield data from the United Kingdom Continental Shelf (UKCS), from which a series of geological, engineering and fluid‐related control variables from 136 fields were pre‐processed and analyzed. This dataset is a subset of a much wider project database for UKCS oil, gas and condensate fields. For this study, the project database was divided into two datasets: a first dataset with 10 parameters from 136 fields, and a second, more detailed dataset with 27 parameters from 38 fields. Both datasets were analysed using principal component analysis in order to investigate possible correlations between numerically/statistically interrogable predictor variables such as porosity, permeability, number of production wells, gas‐oil ratio and reservoir temperature. A regression analysis was then carried out on the predictor variables in order to obtain a ranking of predictability (i.e. how indicative a predictor is of a particular outcome) and sensitivity (how sensitive an outcome is to slight changes in a predictor) in relation to recovery factor based on R‐squared and regression coefficient values. The results showed that key variables from the principal component analysis included field size, number of production wells, PVT, gross depositional environment and reservoir quality. High‐ranking parameters of predictability and sensitivity from the regression analysis were found to include API, net‐to‐gross, porosity and reservoir depth. These results are consistent with previous studies and suggest that it should be possible to forecast oilfield recovery based on only a few selected input variables. As a preliminary test of forecasting ability of the variable permutations put forward, a best‐subsets multiple regression was carried out using a statistical software package and yielded results which corroborated the main findings. [ABSTRACT FROM AUTHOR]

CHILDREN WITH COCHLEAR IMPLANTS (CIS) exhibit large individual differences in vocabulary outcomes. We hypothesized that understudied sources of variance are amount of music engagement and exposure and maternal musicality. Additionally, we explored whether objective measures of music exposure captured from the CI data logs and parent reports about music engagement provide converging and/or complementary evidence, and whether these correlate with maternal musicality. Sixteen children with CIs (Mage = 16.7 months, SD = 7.7, range = 9.6-32.9) were tested before implantation and three, six, and 12 months post-CI activation. Music exposure throughout the first year post-activation was extracted from the CI data logs. Children's vocabulary and home music engagement and maternal musicality were assessed using parent reports. Analyses revealed relatively low home music engagement and maternal musicality. Nonetheless, positive effects emerged for music exposure on children's early receptive and expressive vocabulary and for maternal musicality on expressive vocabulary three months post-activation. Results underline the importance of combining automatic measures and parent reports to understand children's acoustic environment and suggest that environmental music factors may affect early vocabulary acquisition in children with CIs. The presence of these effects despite poor music exposure and skills further motivates the involvement of children with CIs and their parents in music intervention programs. [ABSTRACT FROM AUTHOR]

In some unconventional gas enrichment regions, the topography undergoes extreme undulation, and the surface lithology is considerably altered. This, in turn, has a great impact on seismic exploration. The main difficulty in the acquisition stage has been the selection of effective resonant frequencies and receiving parameters. This study applies a method blending micro logging and refraction to achieve a fine examination of shallow surface structures, as well as to determine a sufficient excitement layer according to the micro logging data, and thus ensure that the grain on the base or consolidated clay layer is generated. The results of this study show that the excitement and reception parameters selected can suppress interference waves, such as surface waves and multiple reflections, and in turn resolve the problem of the low signal-to-noise ratio (SNR) in the mountainous areas of the region, particularly low-noise areas. [ABSTRACT FROM AUTHOR]

Mishrif formation is the main carbonate reservoir in southern Iraq, which is extremely heterogeneous and require accurate geological description for optimal oil field development. This research is associated with developing a representative 3D static model that conserves geological elements and relieves the uncertainty of reservoir properties and volumetric estimates. The interpreted log data and core reports were selected to capture all structural, stratigraphical and petrophysical properties. The reservoir is discretized with irregular cells using the tartan grid, concentrating their accuracy in important areas. A sequential Gaussian simulation algorithm distributed the petrophysical properties for all reservoir units. The structural model showed that Mishrif reservoir in Garraf oil field has a domal structure with two main producing reservoirs (M1.2 and L1.2). The petrophysical model results present that unit L1.2 has high-quality properties with average porosity of 0.24 and water saturation of 0.17 and less than in unit M1.2, where the average water saturation and porosity are 0.47 and 0.185, respectively. The estimated oil in place with the volumetric method was 901 Million standard cubic meters, most of which accumulated in unit L1.2. This study was applied to reappraise the reserves of Mishrif reservoir in Garraf oil field using irregular gridding. [ABSTRACT FROM AUTHOR]

This research presents an unsupervised learning approach for interpreting well-log data to characterize the hydrostratigraphical units within the Quaternary aquifer system in Debrecen area, Eastern Hungary. The study applied factor analysis (FA) to extract factor logs from spontaneous potential (SP), natural gamma ray (NGR), and resistivity (RS) logs and correlate it to the petrophysical and hydrogeological parameters of shale volume and hydraulic conductivity. This research indicated a significant exponential relationship between the shale volume and the scaled first factor derived through factor analysis. As a result, a universal FA-based equation for shale volume estimation is derived that shows a close agreement with the deterministic shale volume estimation. Furthermore, the first scaled factor is correlated to the decimal logarithm of hydraulic conductivity estimated with the Csókás method. Csókás method is modified from the Kozeny-Carman equation that continuously estimates the hydraulic conductivity. FA and Csókás method-based estimations showed high similarity with a correlation coefficient of 0.84. The use of factor analysis provided a new strategy for geophysical well-logs interpretation that bridges the gap between traditional and data-driven machine learning techniques. This approach is beneficial in characterizing heterogeneous aquifer systems for successful groundwater resource development. [ABSTRACT FROM AUTHOR]

The development of fractured oil fields poses a formidable challenge due to the intricate nature of fracture development and distribution. Fractures profoundly impact core resistivity, making it crucial to investigate the mechanism behind the resistivity response change in fracture cores. In this study, we employed the theory of a stable current field to perform a numerical simulation of the resistivity response of single-fracture and complex-fracture granite cores, using a full-size granite core with cracks as the model. We considered multiple parameters of the fracture itself and the formation to explore the resistivity response change mechanism of the fracture core. Our findings indicate that, in the case of a core with a single fracture, the angle, width, and length of the fracture (fracture occurrence) significantly affect core resistivity. When two fractures run parallel for a core with complex fractures, the change law of core resistivity is similar to that of a single fracture. However, if two fractures intersect, the relative position of the two fractures becomes a significant factor in addition to the width and length of the fracture. Interestingly, a 90° difference exists between the change law of core resistivity and the change law of the resistivity logging response. Furthermore, the core resistivity is affected by matrix resistivity and the resistivity of the mud filtrate, which emphasizes the need to calibrate the fracture dip angle calculated using dual laterolog resistivity with actual core data or special logging data in reservoirs with different geological backgrounds. In the face of multiple fractures, the dual laterolog method has multiple solutions. Our work provides a reference and theoretical basis for interpreting oil and gas in fractured reservoirs based on logging data and holds significant engineering guiding significance. [ABSTRACT FROM AUTHOR]

Traditional fracture characterization techniques based on core and imaging logs are too expensive to cover and extrapolate to all wells. In this study, fracture characterization is achieved by comprehensively considering logging responses and geomechanical parameters, combined with the extreme value and variance analysis stratification method, Pearson correlation analysis, principal component analysis (PCA), and long short term memory (LSTM) neural network. The results demonstrate that fracture-developed intervals can be accurately divided by the extreme value and variance analysis stratification method and the logging response. The introduction of geomechanical parameters, Pearson correlation analysis, and PCA can achieve data dimensionality reduction while retaining the information contained in the original indicators, which directly improve the network performance. LSTM network can fully mine the information within and between data, which is highly suitable for fracture characterization. Taking the predicted fracture density as an example, the RMSE is 1.55. Compared with Backpropagation Neural Network (BP), Support Vector Regression (SVR) and Convolutional Neural Network (CNN), the RMSE of this method decreases by 0.08, 0.09 and 0.01, respectively. The findings of this study can help for rationalizing the exploration and development program of carbonate reservoirs and enhance the production of hydrocarbons in naturally fractured reservoirs. [ABSTRACT FROM AUTHOR]

The primary objective of this study is to identify and analyze the petrophysical properties of the newly investigated AEB_IIIG member reservoir in Meleiha West Deep (MWD) Field and to classify it into different rock types. Additionally, this research intends to develop mathematical equations that may be utilized to estimate permeability in uncored sections of the same well or in other wells where core samples are unavailable. The analysis focused on the pore hole records of ten wells that were drilled in MWD Field. The reservoir levels were identified, and their petrophysical parameters were evaluated using well logs and core data. We were able to recognize seven different types of rocks (petrophysical static rock type 1 (PSRT1) to PSRT7) using petrography data, the reservoir quality index (RQI), the flow zone index (FZI), R35, hydraulic flow units (HFUs), and stratigraphy modified Lorenz (SML) plots. The analysis of the petrophysical data shows that AEB_IIIG has unsteady net pay thicknesses over the area. It has a range of 8–25% shale volume, 12–17% effective porosity, and 72–92% hydrocarbon saturation. The RQI results show that psrt1, psrt2 and psrt3 have a good reservoir quality as indicated by high R35 and helium porosity, respectively. They contribute with more than 75% of the reservoir production. The equation derived for each rock type of AEB_IIIG reservoir can be employed to forecast the permeability value distribution inside the reservoir. [ABSTRACT FROM AUTHOR]

To determine if a discrepancy exists in the number and type of cases logged between female and male urology residents. ACGME case log data from 13 urology residency programs was collected from 2007 to 2020. The number and type of cases for each resident were recorded and correlated with resident gender and year of graduation. The median, 25th and 75th percentiles number of cases were calculated by gender, and then compared between female and male residents using Wilcoxon rank sum test. A total of 473 residents were included in the study, 100 (21%) were female. Female residents completed significantly fewer cases, 2174, compared to male residents, 2273 (P =.038). Analysis by case type revealed male residents completed significantly more general urology (526 vs 571, P =.011) and oncology cases (261 vs 280, P =.026). Additionally, female residents had a 1.3-fold increased odds of logging a case in the assistant role than male residents (95% confidence interval: 1.27-1.34, P <.001). Gender-based disparity exists within the urology training of female and male residents. Male residents logged nearly 100 more cases than female residents over 4 years, with significant differences in certain case subtypes and resident roles. The ACGME works to provide an equal training environment for all residents. Addressing this finding within individual training programs is critical. [ABSTRACT FROM AUTHOR]

Conventional methods for estimating porosity from core data are often limited by their spatial coverage, time-consuming nature, high cost, and inability to capture the entire underground reservoir. To address these challenges, this paper proposes a soft computing method using an Adaptive Neuro-Fuzzy Inference System (ANFIS) to estimate porosity in a conventional gas reservoir. The approach involves integrating well-logging data and the ANFIS model with a Particle Swarm Optimisation (PSO) training algorithm to predict the underground porosity model in the Hassi R’mel region of the Algerian Sahara. The choice of this hybrid method was based on its superior performance compared to other models. Although the Hassi R’mel reservoirs are of Triassic clay sandstones, originated by the fluviatile depositional environment that lay on top of the Hercynian surface, the characterisation of their properties still requires refinement to improve the reservoir performance and address the problems faced using appropriate technologies. With an average porosity of 15% and permeability ranging from 250 to 650 mD, the ANFIS method shows excellent accuracy compared to core data, and a reliability of 85%. Overall, the ANFIS-PSO hybrid model proves to be a dependable and efficient technique for porosity prediction, surpassing traditional methods. [ABSTRACT FROM AUTHOR]

Seismic inversion is the primary way to obtain subsurface models, lithologic and stratigraphic information. However, seismic elastic parameters inversion and 'discrete lithofacies' identification for complex volcanic reservoirs are usually independent during the whole inversion process. Also, the influence of reservoir lithology on elastic parameters is not always considered directly before lithofacies prediction. This paper proposes a probabilistic pre-stack seismic inversion method for lithofacies and elastic parameters of volcanic reservoirs. Under the framework of Bayesian inversion, considering that the prior probability distribution of elastic parameters of volcanic reservoirs is affected by volcanic lithofacies, a posteriori probability distribution characterized by a mixed probability model is first derived. Then, a single-point-direct sequential simulation stochastic algorithm with simultaneous optimization of multiple solutions is used to simulate the posterior probability distribution of elastic parameters and lithofacies of volcanic reservoirs, which improves the resolution of lithofacies prediction results of volcanic reservoirs. The feasibility and stability of our method are ensured through synthetic and field applications. The prediction results highly agree with logging curves and lithology logging interpretation data. We have improved the resolution of volcanic rock reservoir lithofacies prediction results. In one-dimensional tests, we achieved the prediction of lithofacies and elastic parameters for three types of volcanic lithofacies. The error compared to prior information is no higher than 15%, thereby verifying the method's good noise resistance. [ABSTRACT FROM AUTHOR]

The Heshui area within the Ordos Basin holds significant strategic importance for the extraction and development of tight oil resources in the Changqing Oilfield. This study extensively explored the geochemical features and distribution tendencies of source rocks in the Chang 7 member, utilizing core samples and logging data for a comprehensive analysis. A more advanced model was utilized to predict the dispersion of Total Organic Carbon (TOC) in the Chang 7 member source rock. The properties and hydrocarbon generation potential of source rocks were thoroughly assessed through a comprehensive analysis that involved evaluating their total organic carbon content, pyrolysis parameters, and reflectance (Ro) values. The research concluded that the source rocks boast substantial organic matter, predominantly categorized as type II-I organic material. The thermal maturation levels span from low maturity to maturity, signifying significant potential for oil generation. Generally, the source rock quality falls within the range of good to excellent. Sedimentary patterns notably influence the distribution of hydrocarbon-source rocks. The northeastern sector of the study area is situated in an area characterized by deep to semi-deep lake sedimentation, making it the primary location for the presence of Chang 7 member hydrocarbon source rocks. With a thickness ranging from 40 to 70 m, this zone becomes a pivotal focus for the potential exploration of tight oil resources in the future. The results of this study offer crucial insights for understanding the geochemical characteristics of hydrocarbon source rocks, evaluating their potential for hydrocarbon generation, and forecasting favorable zones for oil and gas exploration in similar regions. [ABSTRACT FROM AUTHOR]

The Fukang Sag in the Junggar Basin is an important petroleum exploration and exploitation region. However, the geothermal regime and tectono-thermal evolution of the Fukang Sag, which control its hydrocarbon generation and conservation, are still controversial. This study involved a systematic analysis of the present-day geothermal gradient, heat flow, and thermal history of the Fukang Sag for better further exploration. According to the well log data and well-testing temperature data, we calculated that the geothermal gradient of the Fukang Sag ranges from 16.6 °C/km to 29.6 °C/km, with an average of 20.8 °C/km, and the heat flow ranges from 34.6 mWm−2 to 64.3 mWm−2, with an average of 44.6 mWm−2. Due to the basement relief, they decrease from northeast to southwest. The weight averages of the single-grain apatite (U-Th)/He ages of the core samples are 1.3–85.2 Ma, and their apatite fission track ages range from 50.9 Ma to 193.8 Ma. The thermal modeling results revealed that the Fukang Sag experienced late Permian, late Jurassic, and late Cretaceous cooling events (although the timing and magnitude of these events varied among the samples), which were related to the continuous compression of the Junggar Basin. In addition, basin modeling indicated that the heat flow of the Fukang Sag decreased from 80 mWm−2 in the Carboniferous to the current value of 44.6 mWm−2. The Fukang Sag's edge exhibits prolific hydrocarbon generation in the Carboniferous–Permian source rocks, while the Jurassic source rocks within the sag also undergo abundant hydrocarbon generation. This study provides new insights into the present-day geothermal field and tectono-thermal evolutionary history of the Fukang Sag, which are significant in terms of regional tectonic evolution and oil and gas resource assessment. [ABSTRACT FROM AUTHOR]

Porosity, as a key parameter to describe the properties of rock reservoirs, is essential for evaluating the permeability and fluid migration performance of underground rocks. In order to overcome the limitations of traditional logging porosity interpretation methods in the face of geological complexity and nonlinear relationships, the Dynamic Transformer model in machine learning was introduced in this study, aiming to improve the accuracy and generalization ability of logging porosity prediction. Dynamic Transformer is a deep learning model based on the self-attention mechanism. Compared with traditional sequence models, Dynamic Transformer has a better ability to process time series data and is able to focus on different parts of the input sequence in different locations, so as to better capture global information and long-term dependencies. This is a significant advantage for logging tasks with complex geological structures and time series data. In addition, the model introduces Dynamic Convolution Kernels to increase the model coupling, so that the model can better understand the dependencies between different positions in the input sequence. The introduction of this module aims to enhance the model's ability to model long-distance dependence in sequences, thereby improving its performance. We trained the model on the well log dataset to ensure that it has good generalization ability. In addition, we comprehensively compare the performance of the Dynamic Transformer model with other traditional machine learning models to verify its superiority in logging porosity prediction. Through the analysis of experimental results, the Dynamic Transformer model shows good superiority in the task of logging porosity prediction. The introduction of this model will bring a new perspective to the development of logging technology and provide a more efficient and accurate tool for the field of geoscience. [ABSTRACT FROM AUTHOR]

In many subsurface studies, facies and petrophysical properties are two important reservoir parameters that are closely correlated. They are routinely used in well interpretation, hydrocarbon reserve calculation and production profile prediction. These two parameters have commonly been determined in two separate tasks because of their mathematical differences (facies are discrete, and petrophysical properties are continuous). However, this is incorrect because facies and petrophysical properties are often strongly correlated. Therefore, we propose a new joint inversion method of facies and petrophysical properties based on a bi‐level optimization model. In the bi‐level optimization model, the upper‐level problem is the petrophysical property inversion while the lower‐level problem can identify the facies and add a facies‐related constraint for the upper‐level optimization. We also develop a new genetic algorithm for the discrete‐continuous inversion problem based on the bi‐level optimization model because the inversion problem usually has multiple local solutions. In addition, rock physics and statistics are combined in the inversion process. A rock physics model is used to establish the basic relationships between the petrophysical and elastic parameters, and the statistical approach is used to describe the intrinsic connection among the multiple reservoir parameters based on well log data. The numerical experiments indicate that the traditional separate prediction method and the new joint inversion method can quickly obtain more accurate results. In the application examples of real data, the inversion results can be matched to the well log data within the limits of the input data resolution, which further verifies the reliability and application potential of this new method. [ABSTRACT FROM AUTHOR]

Microbial fuel cell (MFC), a bio-electrochemical device that exploits electroactive microbes, has gained more attention in developing countries, such as Indonesia. Unfortunately, studies related to bio-electrochemistry are often constrained due to the need for precise and high-cost instrumentation, such as data logger/acquisition or data logging-multimeter for continuously monitoring electricity generation of MFCs in the rigid time interval. This present work aimed at two issues: (1) to evaluate the use of a low-cost microcontroller-based data logger, the developed multi-channels Arduino UNO-based data logging system, for monitoring the electricity generation of ten MFC bioreactors simultaneously, and (2) to evaluate the electrochemical performance of MFCs biocatalysts by ten electroactive microbes isolated from aquaculture pond sediment in Indonesia. The monitoring system worked with a multi-channels Arduino UNO, a multiplexer, an external 16-bit ADC (analogue to digital converter) ADS1115, and a RTC (real time clock) module. The MFC performance was evaluated in the terms of open circuit voltage and close circuit voltage (polarization curve, power density, and losses). Statistical analysis confirmed the high accuracy of the developed system with the average of absolute and relative error values of 1.21 mV and 1.26%, respectively, comparable to traditional multimeter utilized for MFC electricity measurement. These results suggested that the developed data logging system could be a considerable option as a low-cost monitoring device for electrochemical studies of MFCs. Electrochemical performances of ten anodic biocatalysts were also evaluated, suggesting that there were three effective bacteria (isolate KCf2, KCf4, and KCf10) for producing relatively stable bioelectricity in the reactor of MFCs. These three electroactive microbes can produce power density of 0.069 W m−2, 0.021 W m−2, and 0.010 W m−2, respectively. [ABSTRACT FROM AUTHOR]

Present study aims to locate potential reservoir zones in the Paleocene, Eocene and Oligocene sequences by analysing well log data of nine vertical wells in the exploration block MB-OSN-2004/1 in the Bombay offshore basin, Gulf of Cambay, India. The geophysical logs such as gamma-ray (GR), caliper (Cali), resistivity (LLD, LLS, and MSFL), photoelectric effect (PEE), neutron porosity (NPHI), and density (RHOB) are used to study the petrophysical characteristics of the identified reservoirs in this field. The relevant information from other sources like available report, mud log, and other open hole wireline log data, and local knowledge about the field are integrated with the petrophysical analysis. The cross-plots and mud log data show that the wells mainly consist of three types of lithology: shaly-sand, shale, and limestone, however, coal layers and pyrite are present at some places. Cross plots of thorium and potassium (from spectral gamma-ray log) show that the type of clay is mixed-layers of illite, montmorillonite and kaolinite in the Pipavav and Panna formations. Three of the nine drilled wells C-47-1, MTC-1, and MB-3-1 are identified by the petrophysical investigation as gas-bearing, principally in the Pipavav and Panna formations, with saturation ranging from 55% to 60% and porosity varying from 10% to 40%. Gas-bearing layers' thickness varies from 5 to 40 m and are primarily composed of clastic sediment with mixed carbonates in some locations. The remaining wells are water-bearing because the wells penetrating the silt formations are abundant in clay minerals, which keep the reservoir from releasing fluid and decrease its permeability. In addition, gas cannot rise because impervious shale layers are present in the columns of sand and calcareous sand. [ABSTRACT FROM AUTHOR]

The goal of this study was to detect communicative sequences in mother–baby dyads according to the degree of prematurity, comparing them in three corrected ages (two, four and six months). The observational methodology was used from the mixed methods perspective, and the design is nomothetic/follow-up/multidimensional. Fifteen mother–baby dyads with extreme prematurity and 15 mother–baby dyads with moderate prematurity participated. An observation instrument was constructed combining an observation instrument and category systems to code the dyads' behaviours. Fifteen minutes of free interaction were recorded and Type IV data were logged. The programs HOISAN and SDIS-GSEQ/GSEQ were used to analyse the results. The results detected different behavioural patterns related to the interaction in both the expression of communicative behaviours and in the time when they were deployed in the ages evaluated. [ABSTRACT FROM AUTHOR]

At the Subba oil field, wellbore stability is the main concern while drilling. The wellbore's instability causes several issues, including: (inefficient hole cleaning, tight hole, stuck pipe, mud losses, caving, bad cementing, and well kick or blowout). This increases Non-Productive Time (NPT) and well-drilling costs; hence the operator's main goal is to create a drilling program that reduces these problems and therefore reduce drilling cost. The study aims to build a 1D mechanical earth model to predict the wellbore failure and design optimum mud weight to improve the drilling efficiency for future wells. The model includes pore pressure, stress state, and rock mechanical parameters (such as UCS, angle of friction, Young-Modulus, and PoissonRatio). To achieve this aim, the study utilised offset well data including log data (Gama-Ray Logs (GR), Caliper Logs (CALI), Density Logs (RHOZ), and Compressional Sonic (DTCO) and Shear Sonic (DTSM)), Core tests, Mini-frac field tests, Drilling Reports, Mud Reports, and Mud Log Reports (master log) to estimate and calibrate the profiles of formation pore pressure, rock mechanical properties, and in-situ stresses. The 1D mechanical earth model was built using the Excel program for three wells data set, where all the necessary parameters to create the model was calculated, calibrated for the calculated variables with core data and pressure test points, and finally the safe mud window was detected. The results showed that the Eaton Slowness method to predict pore pressure perfectly matches the pressure test points. The most common fault regimes in the Subba oilfield are normal and strike-slip faults. The Modified Lade criteria showed a compatible match with drilling events and calliper log in predicting the failure zones, so it is the best criterion in determining minimum and maximum mud weight. Based on the results of this study and in comparison with the mud window used in drilling operations in the field, it is necessary to change the mud window used in drilling and adopt the safe MWW of this study in drilling new wells in this field and the area adjacent to the field. [ABSTRACT FROM AUTHOR]

Tight glutenite reservoirs characterization and effective hydrocarbon-bearing formation identification faced great challenge due to ultra-low porosity, ultra-low permeability and complicated pore structure. Fracturing fracture-building technique always needed to obtain deliverability because of poor natural productive capacity. Pore structure characterization and friability prediction were essential in improving such type of reservoir evaluation. In this study, fractured tight glutenite reservoirs in Permian Jiamuhe Formation that located in northwest margin of Junggar Basin, northwest China, were chosen as an example, and 25 typical core samples were drilled and simultaneously applied for mercury injection capillary pressure (MICP), nuclear magnetic resonance (NMR) and whole-rock mineral X-ray diffraction experiments. A novel method of synthetizing pseudo-pore-throat radius (Rc) distribution from porosity frequency spectra was established to characterize fractured formation pore structure. Quartz and calcite were considered as the fragile mineral, and rock mineral component ratio method was used to predict brittleness index. Meanwhile, the statistical model raised by Jin et al. (SPE J 20:518–526, 2015) was used to predict two types of fracture toughness. And then, brittleness index and fracture toughness were combined to characterize tight glutenite reservoirs friability. Combining with maximal pore-throat radius (Rmax, reflected rock pore structure) and friability, our target formations were classified into four clusters. In addition, relationships among pore structure, friability and daily hydrocarbon production per meter (DI) were analyzed, and a model to predict DI from well-logging data was established. Comparison of predicted DI with the extracted results from drill stem test (DST) data illustrated the reliability of our raised models. This would be valuable in determining optimal hydrocarbon production intervals and formulating reasonable developed plans. [ABSTRACT FROM AUTHOR]

Resistivity inversion plays a significant role in recent geological exploration, which can obtain formation information through logging data. However, resistivity inversion faces various challenges in practice. Conventional inversion approaches are always time-consuming, nonlinear, non-uniqueness, and ill-posed, which can result in an inaccurate and inefficient description of subsurface structure in terms of resistivity estimation and boundary location. In this paper, a robust inversion approach is proposed to improve the efficiency of resistivity inversion. Specifically, inspired by deep neural networks (DNN) remarkable nonlinear mapping ability, the proposed inversion scheme adopts DNN architecture. Besides, the batch normalization algorithm is utilized to solve the problem of gradient disappearing in the training process, as well as the k-fold cross-validation approach is utilized to suppress overfitting. Several groups of experiments are considered to demonstrate the feasibility and efficiency of the proposed inversion scheme. In addition, the robustness of the DNN-based inversion scheme is validated by adding different levels of noise to the synthetic measurements. Experimental results show that the proposed scheme can achieve faster convergence and higher resolution than the conventional inversion approach in the same scenario. It is very significant for geological exploration in layered formations. [ABSTRACT FROM AUTHOR]

Porosity, as a key parameter to describe the properties of rock reservoirs, is essential for evaluating the permeability and fluid migration performance of underground rocks. In order to overcome the limitations of traditional logging porosity interpretation methods in the face of geological complexity and nonlinear relationships, this study introduces a CNN (convolutional neural network)-transformer model, which aims to improve the accuracy and generalization ability of logging porosity prediction. CNNs have excellent spatial feature capture capabilities. The convolution operation of CNNs can effectively learn the mapping relationship of local features, so as to better capture the local correlation in the well log. Transformer models are able to effectively capture complex sequence relationships between different depths or time points. This enables the model to better integrate information from different depths or times, and improve the porosity prediction accuracy. We trained the model on the well log dataset to ensure that it has good generalization ability. In addition, we comprehensively compare the performance of the CNN-transformer model with other traditional machine learning models to verify its superiority in logging porosity prediction. Through the analysis of experimental results, the CNN-transformer model shows good superiority in the task of logging porosity prediction. The introduction of this model will bring a new perspective to the development of logging technology and provide a more efficient and accurate tool for the field of geoscience. [ABSTRACT FROM AUTHOR]

Fluid prediction is important in exploration work, helping to determine the location of exploration targets and the reserve potential of the estimated area. Machine learning methods can better adapt to different data distributions and nonlinear relationships through model training, resulting in better learning of these complex relationships. We started by using the convolution operation to process the log data, which includes temporal convolution and spatial convolution. Temporal convolution is specifically designed to capture time series relationships in time series data. In well log data, time information is often critical for understanding fluid changes and other important details. Temporal convolution learns trends and cyclical changes in the data. The spatial convolution operation makes the model more sensitive to the local features in the logging data through the design of the local receptive field and improves the sensitivity to fluid changes. Spatial convolution helps capture spatial correlations at different depths or locations. This can help the model understand the change of fluid in the vertical direction and identify the spatial relationship between different fluids. Then, we use the transformer module to predict the fluid. The transformer module uses a self-attention mechanism that allows the model to focus on information with different weights at different locations in the sequence. In the well log data, this helps the model to better capture the formation characteristics at different depths or time points and improves the modeling ability of time series information. The fully connected structure in the transformer module enables each position to interact directly with other locations in the sequence. By applying it to the data of Tarim Oilfield, the experimental results show that the convolutional transformer model proposed in this paper has better results than other machine learning models. This study provides a new idea in the field of logging fluid prediction. [ABSTRACT FROM AUTHOR]

Fluid prediction is pivotal in exploration, aiding in the identification of targets and estimating reserve potential. To enhance well logging data processing, we employ local linear embedding (LLE) for dimensionality reduction. LLE effectively reduces data dimensionality by identifying local linear relationships and preserving essential local structure in a low-dimensional space, which is particularly advantageous for log data that often contains formation-specific information, including fluid content. The process of dimensionality reduction through LLE retains vital stratigraphic information, which is key for insightful subsequent analyses. Next, we utilize a dynamic graph convolutional network (DGCN) integrated with a multi-scale temporal self-attention (TSA) module for fluid classification on the reduced data. This multi-scale temporal self-attention module is specifically designed to capture time series information inherent in well logging data, allowing the model to autonomously learn and interpret temporal dependencies and evolutionary patterns in the data. This enhances the accuracy of fluid prediction, particularly in the context of varying rock layer characteristics over time. Our methodology, combining LLE with DGCN-TSA, has demonstrated high accuracy in applications such as Tarim Oilfield logging data analysis. It amalgamates advanced technologies with a robust generalization ability. In practical applications, this approach provides steadfast support for oil and gas exploration, significantly contributing to the refinement of fluid prediction accuracy. [ABSTRACT FROM AUTHOR]

The Late Jurassic–Early Cretaceous (J3–K1) transboundary aquifer is the most important groundwater body in southern–southeastern Romania, shared with Bulgaria and hosted in karstic–fractured carbonates. We conducted an integrated evaluation of this aquifer by analyzing three 700 m deep groundwater exploration–exploitation boreholes, which intercepted it in the Cernavodă area (South Dobrogea region). The evaluation was based on geophysical wireline logging, drilling information, and borehole production tests. A K-means clustering of the logging data was performed for lithology typing, formation boundaries identification, and the delineation of probable producing intervals associated with secondary porosity development. Petrophysical interpretation was carried out via depth-constrained (zonal) inversion, using multimineral models, the estimated formation boundaries, and variable uncertainties for the main input logs. The optimal interpretation models were correlated with borehole testing results to gain insight into the hydrogeological properties of the aquifer complex. The fractured–vuggy interval with the highest water-producing potential was identified in the lower section of the J3-age Rasova Formation (639–700 m depth), comprising mainly undolomitized limestones. A southeast-to-northwest trend of increasing productivity of the boreholes, correlated with an increasing lateral dolomitization intensity within the Rasova Formation, suggests a highly heterogeneous character of the aquifer. The differences in productivity are due not only to local porosity variations but also to various degrees of pore space connectivity that are related to the amount of fracturing or karstification. The novel findings of this study have important practical implications for the optimal placement, design, and drilling program of future groundwater exploitation boreholes in the Cernavodă area and neighboring sectors. [ABSTRACT FROM AUTHOR]

Due to the complex nature of the pore system and the diversity of pore types, carbonate rocks pose a challenge in terms of their spatial characterization. Unlike sandstones, permeability in carbonates is often not correlated conclusively with porosity. A methodology for preliminary qualitative spatial characterization of reservoirs in carbonate rocks is presented in this article, with a focus on interparametric relationships. It endeavors to apply this methodology to a reservoir situated within the Main Dolomite formation in the Polish Lowlands. Fundamental analyses rely on data plotted within rock physics templates (RPT), specifically, cross-plots of acoustic impedance as a function of the product of compressional and shear wave velocities in well log profiles. The analysis of interparametric relationships was conducted on well log profiles and subsequently integrated with seismic data using neural network techniques. Areas with the greatest potential for hydrocarbon accumulation and areas potentially exhibiting enhanced reservoir properties were identified based on the outcomes of the well log profile analysis and parametric models. The qualitative assessment of the reservoir, rooted in interparametric dependencies encompassing lithofacies characteristics and elastic and petrophysical parameters, together with reservoir fluid saturation, forms the basis for further, more detailed reservoir analysis, potentially focusing on fracture modeling. [ABSTRACT FROM AUTHOR]

For complex and multi-layered clastic oil reservoir formations, modeling lithofacies and petrophysical parameters is essential for reservoir characterization, history matching, and uncertainty quantification. This study introduces a real oilfield case study that conducted high-resolution geostatistical modeling of 3D lithofacies and petrophysical properties for rapid and reliable history matching of the Luhais oil reservoir in southern Iraq. For capturing the reservoir's tidal depositional setting using data collected from 47 wells, the lithofacies distribution (sand, shaly sand, and shale) of a 3D geomodel was constructed using sequential indicator simulation (SISIM). Based on the lithofacies modeling results, 50 sets of porosity and permeability distributions were generated using sequential Gaussian simulation (SGSIM) to provide insight into the spatial geological uncertainty and stochastic history matching. For each rock type, distinct variograms were created in the 0° azimuth direction, representing the shoreface line. The standard deviation between every pair of spatial realizations justified the number of variograms employed. An upscaled version of the geomodel, incorporating the lithofacies, permeability, and porosity, was used to construct a reservoir-flow model capable of providing rapid, accurate, and reliable production history matching, including well and field production rates. [ABSTRACT FROM AUTHOR]

The advent of open science and the United Nations Decade of Ocean Science for Sustainable Development are revolutionizing the ocean data sharing landscape for an efficient and transparent ocean information and knowledge generation. This blue revolution raised awareness on the importance of metadata and community standards to actionate interoperability of the digital assets (data and services) and guarantee that data driven science preserve provenance, lineage and quality information for its replicability. Historical data are frequently not compliant with these criteria, lacking metadata information that was not retained crucial at the time of the data generation and further ingestion into marine data infrastructures. The present data review is an example attempt to fill this gap through a thorough data reprocessing starting from the original raw data and operational log sheets. The data gathered using XBT (eXpendable BathyThermograph) probes during several monitoring activities in the Tyrrhenian and Ligurian Seas between 1999 and 2019 have been first formatted and standardized according to the latest community best practices and all available metadata have been inserted, including calibration information never applied. Secondly, a new automatic Quality Control (QC) procedure has been developed and a new interpolation scheme applied. The reprocessed (REP) dataset has been compared to the present data version, available from SeaDataNet data access portal through the saved query Url https://cdi.seadatanet.org/search/welcome.php?query=1866&query_code={4E510DE6-CB22-47D5-B221-7275100CAB7F}, processed according to the pioneering work of Manzella et al. (2003) conducted in the framework of the EU Mediterranean Forecasting System Pilot Project (Pinardi et al., 2003). The maximum discrepancy among the REP and SDN data versions resides always within the surface layer (REP profiles are warmer than SDN ones) until 150 m depth, generally when the thermocline settles (from May to November). The overall bias and root mean square difference are equal to 0.002 °C and 0.041 °C, respectively. Such differences are mainly due to the new interpolation technique (Barker and McDougall, 2020), the lack of filtering and the application of the calibration correction in the REP dataset. The REP dataset (Reseghetti et al., 2023; https://doi.org/10.13127/rep_xbt_1999_2019) is available and accessible through the INGV ERDDAP server (http://oceano.bo.ingv.it/erddap/index.html), which allows machine to machine data access in compliance with the FAIR (Findable, Interoperable, Accessible, Reusable) principles (Wilkinson et al., 2016). [ABSTRACT FROM AUTHOR]

Ohmic heating is an emerging technology currently in high demand for application in various processes. In this research, a prototype of a multipurpose ohmic heating apparatus was successfully designed, fabricated, and tested. This apparatus was designed based on low-cost and versatile sensors and components available worldwide. Three independent chambers could be operated parallelly and individually with different treatments. Parameter data, i.e., voltage, electrical current, the temperature of heated material and environmental humidity-temperature, could be recorded by an embedded data logging system. The sensor had been tested and validated by comparing all the sensors used with commercial standard instruments. The result showed that all sensors had high measurement accuracy, indicated by very low mean absolute error (MAE) and mean absolute percentage error (MAPE), with R2 > 0.999. The performance test revealed that product temperature could be accurately maintained according to the set point temperature with a deviation value lower than 0.1 °C. The data logging system was able to record and store the parameter data in SD card memory for up to several days without interruption. The prototype of the ohmic heating apparatus could be an effective alternative to process many purposes such as pasteurisation, cooking, warming, and fermentation based on the ohmic heating principle. [ABSTRACT FROM AUTHOR]

This study presents an integration of various approaches to delimit potential play elements and prospects in the 'Soka' Field Coastal swamp, Niger Delta. This study is a multiple cross-disciplinary work that combined sequence stratigraphy and petrophysical principles to delineate potential reservoirs, seals, and the source rock in the study area. One genetic sequence was identified using seismic sequence stratigraphy and well logs data; the depositional key surfaces, system tracts and their various parasequences were mapped out with the aid of biostratigraphic makers. The result for this study showed that there are two major reservoirs sand that contained hydrocarbon from the well log readings, named top-3 and top-10 reservoirs, regardless of other reservoirs that were encountered. Three major system tracts were encountered, the progradational stacking sediment package of both HST and LST and transgressive sand Package of TST. The depositional environments are of fluvial and deltaic or shallow marine interaction. The following environments were predicted from their various log signatures; they include pro-delta, trangressive marine shelf, slope fan, and tidal channel deposits with this corresponding lithology: shale, sand, heterolith and shaley sand respectively. From the seismic interpretation, it was discovered that this field is highly faulted. The petrophysical analysis revealed that Soka-002 and Soka-004 amongst other wells have much and better reservoir than the rest wells. The average effective porosity is between 20-25%, the average net-to-gross ratio is about 75-75% and hydrocarbon saturation ranges from 65-95%. From the petrophysical crossplot it was discovered that the area of study has good reservoirs capable of habouring hydrocarbon, also the volumetrics estimation showed that Soka-002 and Soka-004 has more and better reservoir zones than the other wells in the field. Generally, the results from sequence stratigraphy, petrophysical analysis and volumetric estimation showed that the study area is highly petroliferous and viable for any economical investments since most of the reservoirs are saturated with either oil or gas. [ABSTRACT FROM AUTHOR]

Wellbore failure criteria are essential issues during drilling deep wells. When drilling activities begin, the major stresses will change, and a new set of forces will be created in the rocks that surround the borehole. These stresses will be caused by the drilling operations themselves. This study concern with estimating stress state and magnitude around the wellbore by constructing a one-dimensional mechanical earth model (1D-MEM) that will help to predict failure criteria during deep wells drilling. A set of well logs data measurement has been used to compute failure criteria parameters for nine formations along the studied well. Repeated formation pressure and laboratory core testing are used to validate the calculated results of 1-D MEM. The prediction of failure criteria along the nine studied formations shows that for Ahmadi, Nahr Umr, Shuaiba, and Zubair formations, the wellbore failure criteria appear unsafe compared to other formations. The results of stress analyses indicate that the breakout factors wasn't affected by wellbore azimuth because of low-stress contrast along the these formations. Furthermore, shear failure can be prevented by drilling the well with an inclination of less than 350. As well as, to prevent breakdown the well should be drilled with an inclination between 25o to 65o in the direction of minimum horizontal stress. These important results could be used to pridict accurate wellbore trajectory when planning to drill nearby wells in the future. [ABSTRACT FROM AUTHOR]

The Cambrian system in the Ordos Basin has good exploration potential, and the Zhangxia Formation and the Sanshanzi Formation of the Middle and Upper Cambrian are important targets for the exploration of the Cambrian system. In this paper, the characteristics, genesis and main controlling factors of the Zhangxia Formation and the Sanshanzi Formation reservoir are studied through field section observation, thin section observation, drilling and geophysical logging data combined with experimental analysis data. The reservoir space types of the Cambrian carbonate rocks in the Ordos Basin mainly include intercrystalline pores, intercrystalline dissolution pores, dissolution pores, dissolution sutures and dissolution fractures, among which intercrystalline dissolution pores, dissolution pores and dissolution fractures constitute the most important reservoir space type. There are three reservoir types: pore type, fracture - dissolution cavity type and fracture type. The development of Cambrian strata in Ordos Basin is mainly controlled by three factors: high-energy sedimentary facies zone, epigenetic diagenesis and tectonic movement. Granular beach facies and dolomite tidal flat facies are favorable zones for reservoir development in Zhangxia Formation and Sanshanzi Formation. The fault zone in the basin provides a migration channel for late hydrothermal fluid, which can significantly improve reservoir performance. Tectonic fracture itself is an effective pore system, which becomes the migration channel of geological fluid, and also plays a role in communicating pores, which plays a positive role in reservoir. [ABSTRACT FROM AUTHOR]

Fractures play a crucial role in discovering and developing petroleum reserves. However, traditional logging techniques face significant challenges in identifying fractures. To address such challenges, this article proposes a new method that combines conventional logging data with a small amount of marker data from cores and image logs to identify fracture development in reservoirs with high accuracy and fast operation. The proposed method is based on the improved YOLOv5, which offers a new idea for fracture identification. The fracture data from the carbonate rocks of the Sulige gas field in the Ordos Basin were used for training and validation. Finally, positive experimental outcomes were achieved, demonstrating the usefulness of the improved YOLOv5 algorithm in detecting fracture development in carbonate rocks. [ABSTRACT FROM AUTHOR]

The Zubair Formation is one of the major reservoirs of high production in the Rumaila oilfield, southern Iraq. The petrophysical properties analysis of the Upper Sand Member (Main Pay) of the Zubair Formation was conducted. The study includes results analysis of four wells distributed along the South Rumaila oilfield. Using a set of open well-logs, the main pay was divided into three main pay (AB, DJ and LN) units separated by two insulating shale units (C and K). The unit DJ was subdivided into three secondary reservoir units: D, F, H and the LN unit, which is split into L, M, and N. The research also includes the statistical analysis of the petrophysical properties, the calculation of the heterogeneity of the reservoir, and the cluster analysis of the upper sand member. The results indicated that the petrophysical specifications are good. Whereas, the results of the statistical analysis showed that the study wells were heterogeneous reservoirs that could be and were divided into four facies (Sand, Shaly Sand, Sandy Shale and Shale) depending on the log data. [ABSTRACT FROM AUTHOR]

Recently developed technology in sawmills such as advanced log scanning and traceability concepts enable new ways of grading logs and boards. When it comes to strength grading, this is often done on sawn boards using automatic scanning systems. However, if board scanners were to be augmented with data from log scanners by using traceability, more information on the wood properties is available. In this study, the main objective was to compare the strength prediction capability of board scanning alone, to board scanning augmented with X-ray and 3D data from log scanning, for Norway spruce (Picea abies L. Karst.) and Scots pine (Pinus sylvestris L.). In that case, data from three different scanning systems was combined, two for logs and one for boards. A further objective was to investigate whether pre-sorting logs for strength grading can be done using either 3D log data alone, or 3D log data augmented with X-ray data. The results show an improved strength prediction when adding log data to board data, and that 3D log data alone is not enough to pre-sort logs for strength, while adding X-ray log data makes it possible. Strength prediction on Scots pine performed somewhat better than prediction on Norway spruce. [ABSTRACT FROM AUTHOR]

Accurately predicting the fluids holds immense significance in exploration work, assisting in the identification of exploration targets, estimation of reserve potential, and evaluation of reservoirs. In our research, we employed an innovative approach by using the gram angle field (GAF) to transform logging parameters. By adeptly capturing time series information and converting one-dimensional data into two-dimensional matrix representations, GAF takes into account not only the values at each time point but also their relative position and order. This method effectively preserves the temporal evolution characteristics of the original data. The resulting Gram Angle Field matrix can be viewed as a two-dimensional image, facilitating visualization and analysis through image processing techniques. Additionally, we introduced the dynamic graph convolutional network (DGCN) to segment the transformed images. The DGCN structure, employed for feature learning, can extract more comprehensive and representative feature representations from the logging data. Since logging data demonstrate a time series relationship, indicating a temporal correlation between logging curves at different depths, DGCN utilizes dynamic graph structures to capture and comprehend this time series information. This capability enables DGCN to model the evolution process of well log data effectively. DGCN assigns varying weights to nodes and edges at each time step, updating the current node representation with information from neighboring nodes. This localized approach enables DGCN to meticulously focus on significant features at each time step, facilitating the identification of potential patterns and trends in the logging data. Our research not only paves the way for advancements in the field but also provides valuable insights for geologists and professionals engaged in oil and gas exploration. [ABSTRACT FROM AUTHOR]

Fluid prediction is important in exploration work, helping to determine the location of exploration targets and the reserve potential of the estimated area. Machine learning methods can better adapt to different data distributions and nonlinear relationships through model training, resulting in better learning of these complex relationships. We first use the Gram angle field (GAF) to convert one-dimensional logging data into two-dimensional images. GAF can better capture the nonlinear structure and patterns in time series data by using trigonometric transformation. After that, we used the Swin Transformer model to classify the converted images. It captures the locality and timing of the image by moving the window. Swin Transformer uses a staged attention mechanism that allows the model to efficiently capture feature information at different scales. This allows the model to capture both local and global information in the image, contributing to a better understanding of the image content. The multi-scale feature capture capability of the Swin Transformer enables it to effectively capture different scales and spatial relationships in fluid prediction tasks. Tested in real data from Tarim Oilfield, the GAF-Swin Transformer model has better performance than other machine learning models. This study provides a new perspective in the field of fluid prediction. [ABSTRACT FROM AUTHOR]

Given the diverse range of fluid types in reservoirs, their frequent alternation, and complex composition, traditional methods exhibit low accuracy in identifying these types. To address this, we introduce machine learning techniques to predict fluid types by extracting logging data. However, a single Gate Recurrent Unit (GRU) network is insufficient to meet the demands of fluid type prediction. Therefore, we propose a method that combines the GRU network with the Adaboost algorithm, referred to as GRU-Adaboost. The GRU-Adaboost model effectively combines multiple weak classifiers into a more powerful classifier through iterative training and gradual adjustment of sample weights. By using a voting strategy to synthesize the predictions of individual classifiers, the impact of errors from each classifier can be reduced. Compared with traditional GRU networks and Long Short-Term Memory models, the proposed GRU-Adaboost model shows improved accuracy. To validate the feasibility of our method, we apply the proposed algorithm to three wells. Experimental results confirm that the prediction performance of GRU-Adaboost surpasses that of other models. [ABSTRACT FROM AUTHOR]

Well logging fluid prediction is of great significance in oil and gas exploration. Based on data mining technology, this paper proposes an adaptive boosting random forest (Adaboost-RF) method for well logging fluid prediction. First, we use the Adaboost algorithm for feature selection, train a weak classifier by repeatedly weighting observations and correcting hard-to-classify samples, and obtain a combination of multiple weak classifiers. This method can effectively improve the accuracy and robustness of the classifier and can reduce the risk of overfitting. Then, we use random forest (RF) as a basic classifier to build an Adaboost-RF model for well logging fluid prediction. The combination of Adaboost and RF can further improve the stability and accuracy of the classifier. To verify the performance of this method, we performed experimental evaluation using real well logging data. Experimental results show that the Adaboost-RF method can have higher accuracy and stability in log fluid prediction than the traditional method (backpropagation neural network) and the method using RF alone. In summary, this method combines the characteristics of Adaboost and RF, which can improve the accuracy and stability of the classifier and is easy to implement and generalize, providing a new, efficient, and accurate fluid prediction method for the field of oil and gas exploration. [ABSTRACT FROM AUTHOR]