Porosity Logs Research Articles

The DQ Trace Method for Quantitative Seismic Interpretation: Theory and Application to a Synthetic AVO Data Catalog

The Distance and Quadrant (DQ) trace is a novel seismic attribute that enhances the detection and visualization of hydrocarbons in seismic data. Generated through a combination of partial near and far stack traces convolved with phase shift filters, the DQ trace offers several advantages over traditional AVO attributes and inversion methods. This study presents the DQ trace generation process, which involves quadrant optimization and the application of a modified AVO two-layer model equation. The method is computationally efficient and does not require a priori knowledge of petrophysics, basin geology, or wavelets. Concurrent phase angle analysis is used to compare DQ traces with porosity logs, demonstrating a strong correlation between the two. Synthetic models for different AVO classes are employed to evaluate the performance of DQ traces in cross-section displays. The results show that DQ traces provide enhanced clarity and delineation of gas sands, wet sands, and thin beds, as well as improved visualization of tuning effects and fluid contacts compared to conventional seismic displays. DQ histogram analysis reveals systematic trends across AVO classes and allows for the separation of gas sands, wet sands, and shales. The DQ trace is a robust tool for porosity estimation and has potential applications in various seismic processes and basin-scale mapping. This study highlights the implications of the DQ trace for fluid detection and seismic interpretation, offering a new methodology in subsurface reservoir characterization.

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.

Microfacies Analysis and Evolution of Porosity of the Albian-Early Cenomanian Mauddud Formation from Selected Wells in Ratawi Oilfield, Southern Iraq

Mauddud Formation (Albian stage-the Early Cretaceous) is an important oil reservoir in Ratawi field of southern Iraq. Four wells, R T-2, R T-3, R T-6, and R T-7, located 70 km northwest of Basra, were selected to study microfacies properties and petrophysical associations with the probability of oil production. Seventy-seven core samples are collected, and thin sections for petrographic analysis. The self-potential, Gamma-ray, resistivity, and porosity logs are used to determine the top and bottom of the Mauddud Formation. Water saturation of the invaded and uninvaded zones, shale volume, and porosity were calculated. The study area results showed that the quantity of shale is less than 15% for most of the wells, and the dominant porosity is the secondary porosity. In contrast, the primary porosity is low in all study wells, and the formation contains varying proportions of producing hydrocarbons. The results suggest the formation comprises light-colored dolomitized lime and pseudo-oolitic white limestone with green to blue-gray shale. The petrographic analyses of 65 thin sections of the Mauddud Formation reveal that most skeletal grains are shallow marine-derived faunas, while non-skeletal grains include peloids and ooids. The facies analysis showed that the Mauddud Formation was deposited within different sedimentary environments ranging from deep to very shallow environments, which was represented by a large variation in Five microfacies and ten submicrofacies, the microfacies "Mudstone, wackestone, packstone, grainstone, and Rudstone "submicrofacies" Peloidal wackestone to packstone, Bioclastic wackestone to packstone, Miliolids wackestone to packstone,Orbitolina wackestone, Peloidal packstone to grainstone, Orbitolina packstone, Mudstone to wackestone,Bioclastic wackestone, Miliolids and Bioclastic wackestone to packstone, Bioclastic packstone, and Peloidai grainstone". This variance suggested that the formation might be deposited within the carbonate platform's variety of environment, such as "deep sea, restricted, shallow open marine, mid-ramp, rudist biostrome, and shoal." Seven different kinds of pores have been found in the carbonate sideman of the Formation: Interparticle, Intraparticle, moldic, Vuggy and fracture.

S-wave log construction through semi-supervised regression clustering using machine learning: A case study of North Sea fields

Accurate prediction of S-wave velocity from well logs is essential for understanding subsurface geological formations and hydrocarbon reservoirs. Machine learning techniques, including clustering and regression, have emerged as effective methods for indirectly estimating S-wave logs and other rock properties. In this study, we employed clustering algorithms to identify similarities among well log datasets, encompassing depth, sonic, porosity, neutron, and apparent density, facilitating the discovery of correlations among various wells. These identified correlations served as a foundation for predicting S-wave values using a novel semi-supervised approach. Our approach combined clustering, specifically k-means clustering, with different types of regressors, including Least Squares Regression (LSR), Support Vector Regression (SVR), and Multi-Layer Perceptron (MLP). Our results demonstrate the superior performance of this integrated approach compared to traditional regression methods. We validated our methodology using various parametric and non-parametric regression techniques, showcasing its effectiveness not only on wells within the training region but also on wells outside the study area. We achieved a significant improvement in the R2 score metric, ranging from 2.22% to 6.51%, and a reduction in Mean Square Error (MSE) of at least 31% when compared to predictions made without clustering. This study underscores the potential of machine learning techniques for accurate prediction of S-wave velocity and other rock properties, thereby enhancing our comprehension of subsurface geology and hydrocarbon reservoirs.

Advancing Reservoir Evaluation: Machine Learning Approaches for Predicting Porosity Curves

Porosity assessment is a vital component for reservoir evaluation in the oil and gas sector, and with technological advancement, reliance on conventional methods has decreased. In this regard, this research aims to reduce reliance on well logging, purposing successive machine learning (ML) techniques for precise porosity measurement. So, this research examines the prediction of the porosity curves in the Sui main and Sui upper limestone reservoir, utilizing ML approaches such as an artificial neural networks (ANN) and fuzzy logic (FL). Thus, the input dataset of this research includes gamma ray (GR), neutron porosity (NPHI), density (RHOB), and sonic (DT) logs amongst five drilled wells located in the Qadirpur gas field. The ANN model was trained using the backpropagation algorithm. For the FL model, ten bins were utilized, and Gaussian-shaped membership functions were chosen for ideal correspondence with the geophysical log dataset. The closeness of fit (C-fit) values for the ANN ranged from 91% to 98%, while the FL model exhibited variability from 90% to 95% throughout the wells. In addition, a similar dataset was used to evaluate multiple linear regression (MLR) for comparative analysis. The ANN and FL models achieved robust performance as compared to MLR, with R2 values of 0.955 (FL) and 0.988 (ANN) compared to 0.94 (MLR). The outcomes indicate that FL and ANN exceed MLR in predicting the porosity curve. Moreover, the significant R2 values and lowest root mean square error (RMSE) values support the potency of these advanced approaches. This research emphasizes the authenticity of FL and ANN in predicting the porosity curve. Thus, these techniques not only enhance natural resource exploitation within the region but also hold broader potential for worldwide applications in reservoir assessment.

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

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

Pilot Area Formation Evaluation: Upper Shale Member/ Rumaila Oil Field

This study aims to conduct a comprehensive formation evaluation of a pilot area within the Upper Shale Member of the Rumaila Oil Field. This evaluation is an essential step in the full development of the field. The application of well-log data and core analyses can help in obtaining the desired information about the geological characteristics of the formation. The process begins with measuring the formation temperature and water resistance utilizing Schlumberger’s charts and equations. The volume of shale was determined by two different methods, which were then used together to obtain the final shale volume. The porosity was determined using the conventional porosity equations from the porosity logs and the saturation was estimated based on Archie's equation. In core data analysis, an unconventional technique was utilized to determine rock type and permeability. The core porosity and the permeability were classified into four groups mainly using a self-organizing map and an unsupervised machine-learning method, and selected regression equations of each group were applied to estimate permeability in the core. The method depicted a good agreement between the core and estimated permeabilities, proving it as an effective tool. A complicated training data set was constructed based on the use of a multilayer perceptron neural network on coreless wells to identify rock types and permeability. Analyzing the petrophysical properties of the study area showed evidence that this area is characterized by heterogeneity. The heterogeneity of this formation is due to the presence of a considerable amount of shale, in addition to the significant characteristic differences in the layers and the same layer in different locations. The abundance of shale rock poses challenges during drilling operations, particularly due to shale washout which can lead to mechanical issues with the drilling string. Therefore, caution is advised when drilling new wells in the area to mitigate shale washout risks. Furthermore, the analysis identified layers with high hydrocarbon saturation that are viable for production. Conversely, some layers, characterized by shale presence and poor rock quality, are deemed unsuitable for production, and should not be considered as reservoir rock.

Hyperparameter inversion of well logs for the simultaneous estimation of volumetric and zone parameters

We develop a new alternative for the joint inversion of well logs to predict the volumetric and zone parameters in hydrocarbon reservoirs. Porosity, water saturation, shale content, kerogen, and matrix volumes are simultaneously estimated with the tool response function constants with a hyperparameter estimation assisted inversion of the total and spectral natural gamma-ray intensity, neutron porosity, and resistivity logs. We treat the zone parameters, i.e., the physical properties of rock matrix constituents, shale, kerogen, and porefluids, as well as some textural parameters, as hyperparameters and estimate them in a metaheuristic inversion procedure for the entire processing interval. The selection of inversion unknowns is based on parameter sensitivity tests, which indicate that the automated estimation of several zone parameters is favorable, and their possible range can also be specified in advance. In the outer loop of the inversion procedure, we use a real-coded genetic algorithm for the prediction of zone parameters, and we update the volumetric parameters in the inner loop in addition to the fixed values of the zone parameters estimated in the previous step. We apply a linearized inversion process in the inner loop, which allows for the quick prediction of volumetric parameters along with their estimation errors from point to point along a borehole. The derived parameters, such as hydrocarbon saturation and total organic content, indicate good agreement with core laboratory data. The significance of the inversion method is that the zone parameters are extracted directly from the wireline logs, which improves the solution of the forward problem and reduces the cost of core sampling and laboratory measurements. In a field study, we demonstrate the feasibility of the inversion method using real well logs collected from a Miocene tight gas formation situated in the Derecske Trough, Pannonian Basin, East Hungary.

Volve Oil Field S-Wave Log Data Prediction Using GBR and MLPR

The shear wave sonic (S-wave) log data is essential for identifying the reservoir's geomechanical properties, which is an important factor for the drilling, completion, and optimization processes, where obtaining S-wave requires capital investment and reduces the cost. In this paper, we used the multi-layer perceptron (MLPR) and the gradient boosting regression (GBR) to predict the missing S-Wave log data for the Volve Oil Field in the North Sea. Prescriptive and predictive analysis were carried out in series to achieve a high blind data accuracy rate of 0.943 and 0.982 for the multi-layer perceptron regression and the gradient boosting regression, respectively. It was observed that the gradient-boosting algorithm achieved higher accuracy than the MLPR algorithm for the limited dataset. It was also found that hierarchical clustering can reveal information regarding the feature's importance similar to the relevant AI tools, which makes hierarchical clustering a faster tool in eliminating the nonimportant inputs from the dataset while the use of artificial intelligence tools showed a significant effect in predicting the missing values of the sonic wave log and the neutron porosity log in an efficient way by selecting the relevant important features.

Reservoir Characterization of KD Field Located Onshore of Akwa-Ibom State, Niger Delta, Nigeria Using Well Logs Property Comparison to Seismic Amplitude Analysis

Characterization of reservoirs defines how well they can generate and store hydrocarbon, reservoir parameters are used to determine the behavior of reservoir fluids under various conditions and to identify the best production practices that can optimize hydrocarbon production, the objective is to identify and map reservoir sand zones using basic logs, provide information about the reservoir depth and thickness using well log data, interpret faults and sealing system using the seismic data and generate amplitude maps and model basic reservoir facies, the method applied was sectioned into three distinct parts that encompasses qualitative well log analysis, seismic interpretation and cross plotting of basic well logs versus amplitude for reservoir characterization, the well data analyzed shows well number 5, 6 and 8 have incomplete GR logs resulting in missing lithology while well 10 have some missing logs, such as, density (DEN) log, neutron porosity (NEU) log and porosity (POR) log, five logs needed for this study were available in the remaining complete wells. These logs include gamma ray (GR) log, resistivity (RES) log, density (DEN) log, neutron porosity (NEU) log, and porosity (POR) log. This study looks at a possible change in log signal due to an increase or decrease in seismic amplitude in characterizing a reservoir sand and how logs are influence with Amplitude peaks and trough.

Reservoir Characterization of the Paleogene Khurmala Formation in Tawke and Shaqlawa Areas, Kurdistan Region of Iraq

Well logs were utilized to investigate petrophysical properties of the Khurmala Formation’s surface outcrops in Shaqlawa Subdistrict and Tawke Oilfield, e.g., lithology, shale volume, porosity, and fracture. The thickness of the formation is about 15 m in the Shaqlawa section and 42 m in the Tawke Oilfield. Porosity logs were used to estimate porosity; where the porosity values reached a maximum of 52% from the sonic log, 48% from the density log, and 35% from the neutron porosity log. The reservoir quality of the Khurmala Formation, as determined through the analysis of thin sections, which were obtained from outcrop samples, is deemed to be of low quality. The determined shale volume within the examined interval exhibits a moderate level of clay constituents, with the highest gamma-ray measurement indicating a shale content of 29% at some locations within the reservoir. This integrated method using various conventional well logs suggests a great probability of petrophysical properties in the Khurmala Formation to be considered as the reservoir.

Fracture Density Prediction of Basement Metamorphic Rocks Using Gene Expression Programming

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.

Poro-Acoustic Impedance (PAI) as a new and robust seismic inversion attribute for porosity prediction and reservoir characterization

For the purpose of reservoir modelling, precise porosity estimation is vital as it directly influences the storage capacity, fluid flow dynamics, and overall productivity of the reservoir. The computation of porosity is a key component of reservoir characterization. The Poro-Acoustic Impedance (PAI), a seismic inversion attribute, has proven to be effective for porosity estimation in hydrocarbon reservoirs. PAI, an extended version of Acoustic Impedance (AI), incorporates porosity information directly, enhancing its utility in forward modelling and seismic data inversion. In this study, offshore oil resources in Iran were examined, focusing on two components: sandstone and carbonate. The results of AI and PAI were compared, indicating that PAI is a suitable attribute for estimating porosity. The correlation between porosity and AI was −45%, while it was −74% with PAI. Moreover, the synthetic seismogram created using PAI aligns more closely with real seismograms. Porosity was estimated using both AI and PAI, with a 72% correlation between the porosity estimated using AI and the actual porosity. However, the correlation increased to 78% when using PAI. Furthermore, the porosity section was calculated using both AI and PAI, concluding that the PAI porosity section aligns more closely with the porosity log and provides a greater contrast in low porosity zones compared to AI. Given that porosity is incorporated into the PAI formula, the PAI porosity section and inversion results can be used as an indicator for evaluating the hydrocarbon capacity of the reservoir. The findings of this research suggest that PAI is an effective attribute for porosity estimation, bridging the gap between seismic data and porosity estimation, thereby enhancing our understanding and exploration of the reservoir.

Quantitative Analysis of Diagenesis Control on the Spatial Heterogeneity of Sarvak Carbonate Formation, the Dezful Embayment of Zagros Basin, Western Iran

Summary The heterogeneity indices in conventional carbonate reservoirs are usually influenced by various diagenetic processes. The main aim of this study is to build 3D geological models of the diagenetic and heterogeneity indices addressing the role of diagenesis on spatial heterogeneity variations in the Sarvak carbonate reservoir of an Iranian oil field situated in the Dezful Embayment in the Zagros Basin. Vertical heterogeneity has been discussed in many studies, but the literature is limited on the 3D geological modeling of heterogeneity and providing spatial heterogeneity maps. In this study, we determined the main diagenetic parameters influencing the reservoir heterogeneity and constructed 3D geological models for evaluating the spatial heterogeneity. A couple of heterogeneity indices including the coefficients of variation (CVs) of porosity and permeability, Lorenz coefficient, and Dykstra-Parsons coefficient in cored intervals along with some diagenetic parameters were calculated and modeled. This study shows that lateral heterogeneity is mappable and correlatable with different diagenetic overprints along with shale volume, which controls the reservoir quality and spatial heterogeneity. It also indicates that lateral trends of the heterogeneity indices in different zones of the studied reservoir are influenced by other diagenetic parameters such as secondary porosity (SPI), dolomitization, cementation, and velocity deviation log (VDL, as a diagenetic index). Different diagenetic parameters control reservoir heterogeneity in each zone by decreasing or increasing the degree of the heterogeneity, some of which have dual constructive or destructive effects on heterogeneity indices. Moreover, the results show that the effect of shale volume on reservoir heterogeneity is significant, especially when the amount of the shale volume is notable.

A New Empirical Correlation for Pore Pressure Prediction Based on Artificial Neural Networks Applied to a Real Case Study

Pore pressure prediction is a critical parameter in petroleum engineering and is essential for safe drilling operations and wellbore stability. However, traditional methods for pore pressure prediction, such as empirical correlations, require selecting appropriate input parameters and may not capture the complex relationships between these parameters and the pore pressure. In contrast, artificial neural networks (ANNs) can learn complex relationships between inputs and outputs from data. This paper presents a new empirical correlation for predicting pore pressure using ANNs. The proposed method uses 42 datasets of well log data, including temperature, porosity, and water saturation, to train ANNs for pore pressure prediction. The trained model, with the Bayesian regularization backpropagation function, predicts the pore pressure with an average absolute percentage error (AAPE) and correlation coefficient (R) of 4.22% and 0.875, respectively. The trained ANN is then used to develop a new empirical correlation that relates pore pressure to the input parameters considering the weights and biases of the optimized ANN model. To validate the proposed correlation, it is applied to a blind dataset, where the model successfully predicts the pore pressure with an AAPE of 5.44% and R of 0.957. The results show that the proposed correlation provides accurate and reliable predictions of pore pressure. The proposed method provides a robust and accurate approach for predicting pore pressure in petroleum engineering applications, which can be used to improve drilling safety and wellbore stability.

Application of the dynamic transformer model with well logging data for formation porosity prediction

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.

Friction Angle Prediction of Carbonate Rocks: A Case Study, Rumaila Oil Field

Friction angle (𝜑) and Cohesion (𝑐) are the most important factors to depict rock's shear strength. The friction angle (φ) expresses a unit of rock's capacity to endure shear stress. For the optimization of drilling operations, monitoring of the reservoir, and production of hydrocarbons, the prediction of friction angle is essential. From laboratory measurements or wireline logging data, this parameter can be empirically predicted. The main goal of this study is to develop a new correlation for predicting friction angle for carbonate formations from well logs using the typically accessible well log data (i.e. neutron porosity, gamma ray, bulk density, and sonic logs) and core data. A total of 5197 well log data points were collected from carbonate formation with depth interval of (1920 m to 2711 m) from Rumaila oil field. For all 5197 data points neutron porosity, and gamma ray logs were recorded as a function of depth, and the corresponding shale volume and total porosity were estimated. In addition to these well log data, 20 data core points with 9 different values of friction angle were collected. The developed correlation's estimated friction angle has been contrasted with measured ones. The results show that the new correlation is able to predict the friction angle of carbonate rocks with high accuracy (i.e. R coefficient of the new correlation was 90% and average absolute error of 1.6%).Thus, we conclude that the new correltion can be used to estimate the friction angle for carbonate formation. The new correlation helps in providing continues profile for friction angle with depth and leads to reduce the cost of estimating the rock strength.

Well Logs Analysis and Petrophysical Properties of Shiranish Formation in the East Baghdad Oil Field, Central Iraq

The objective of this study is to characterize of the petrophysical properties of Shiransih Formation in East Baghdad oilfield. The analysis involves both of examination of Well Log data obtained from specific wells, namely EB-86, EB-84, EBSK-10-4, EBSK8-2, and EBSK-5-4. The Gamma Ray Log is utilized to quantify volume of shale within the formation, while Porosity Logs, including Density Log, Neutron Log, and Sonic Log, are employed to determine effective porosity and index secondary porosity. Water saturation, movable hydrocarbon, and residual hydrocarbon are calculated using Resistivity logs (LLd, MSFL, and ILD). Additionally, a cross-plot analysis is performed utilizing Density Log and Neutron Log to determine the lithology of the Shiransih Formation.

Porosity prediction through well logging data: A combined approach of convolutional neural network and transformer model (CNN-transformer)

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.

Well Logs Analysis and Reservoir Evaluation of Hartha Formation in the East Baghdad oil field central Iraq

This study objective of ascertaining various petrophysical properties, which encompass volume shale, porosity, and water saturation. Well logging assumes a pivotal role in the comprehensive evaluation of reservoirs by providing measurements that can be effectively correlated with the volume fraction and composition of hydrocarbons within porous geological formations.In the course of this research, the quantification of shale volume was achieved through the utilization of gamma-ray logs, while the determination of effective porosity was facilitated by employing porosity logs, including the density log, neutron log, and sonic or acoustic log. The Hartha Formation has been stratified into distinct reservoir units based on their petrophysical characteristics. The interpretation of the findings involved the direct examination of well log readings, complemented by the application of well-established relationships and profiles to discern the critical lithological and petrophysical attributes inherent to the Hartha Formation. The selected wells from the East Baghdad oilfield, namely EB-93, EB-97, EB-51, and EB-42.