Well Logs are principal sources of subsurface geoLogical information. They provide significant information on mineraLogical composition, texture, sedimentary structures and petrophysical properties such as porosity and permeability. By compiling data from various well Logs, one can discriminate sedimentary units with comparable Log characteristics. Sedimentary units with similar fluid flow and capacity are named rock type. Rock type determination is the most important task in reservoir characterization of oil bearing formations. Rock type may be determined using different data sets but their definition on the basis of wire line Logs is most common. Multivariate cluster analysis (as the best method of data grouping) is one of the most accurate and effective methods in oil bearing reservoir zonation. The method is applied on both detrital and carbonates rocks. This method gets more support by improvements in algorithms and statistics. Proper combination of Logs and appropriate algorithm will increase the accuracy, reliability and effect of the method. Similar faces may have different Log responses due to diverse factors that affect the Logs. Since using statistical methods and procedures are mandatory, in clustering procedure data are grouped with minimum distance and maximum homogeneity. It is obvious that distinct geoLogical parameters can be related to a group of data, which are to be used by geoLogists for further interpretation. For this calculation, all Log readings are considered as "observations" and the used Logs as the "values of the observations".There are several ways to compute the distance between objects. The "Standardized Euclidean" distance is used here in form the MATLAB software, because more accurate results are obtained with this procedure. By grouping Log data in multidimensional space (equal dimensions with number of Logs), each point (reading) can be related to a group of data (rock type). High resolution rock typing with reliable conclusions can be inferred with this procedure using pure mathematical formula in which there is no need to regression equations or trainings. In this method, any geoLogical parameter described from other sources such as cores and thin sections can be related to wells with comparable rock types. The accuracy and reliability of defined rock types can be examined in wells from which suitable cores are available. Results from such a comparison provide a fundamental base for study of wells with poor core and cutting data.Using MATLAB software, this study testifies a new simple method for rock type determination of Asmari Formation in Marun Field. The reliability of the method is examined by correlation of the resultant rock types with those of inferred from cores. Result from such a correlation indicates the reliability of method in rock type determination.

With the rapid rise of cybersecurity threats and the increasing complexity of digital security, event Log data serves as a critical source for identifying and analyzing cyberattacks and threats. This data provide key insights into system activities, essential for detecting unauthorized intrusions, analyzing suspicious behaviors, and conducting security investigations. However, any alteration or tampering with the data can disrupt the analysis and detection processes, leading to incorrect security decisions. Blockchain technoLogy, with its unique features such as decentralization, immutability, and transparency, has been recognized as a reliable and secure platform for storing and protecting data. This technoLogy enables the storage of data hashes in a way that any changes can be easily detected. However, directly storing the vast volume of event Log data on the blockchain faces challenges such as high costs and storage space limitations. In this research, an innovative model has been presented to automate the assurance of event Log data integrity and confidentiality using the public Ethereum blockchain and smart contracts. Instead of storing event Log data directly, only their hashes have been saved on the blockchain. This approach not only reduces storage costs but also ensures data confidentiality. The automated data integrity assurance process in this model occurs in two stages: Stage One: Event Log data hashes have been periodically stored on the blockchain and compared with previous hashes. Stage Two: Over longer intervals, all stored hashes have been reviewed and validated to prevent any potential tampering. In this study, the costs associated with implementing this model on the Ethereum Sepolia test network had been precisely calculated. The analysis indicates that operational costs and computational overhead have been optimized across different time intervals, demonstrating the model's feasibility for large-scale deployment. Ultimately, this research tries to introduce a novel and practical model, taking a significant step toward automating the assurance of event Log data integrity and confidentiality, providing a reliable solution for real-world applications.

We use core analysis and well testing to determinate the reservoir lithoLogy. Unfortunately, coring from each wells in large oil fields such as Iran oil fields, is very expensive. However, because of the importance of this information which is obtained from lithoLogy, it is necessary to coring from some of the reservoir wells.Purpose of this study is prediction of hydrocarbon reservoir lithoLogy in South Pars field using artificial neural network with back propagation error algorithm (BP) and Trainlm algorithm with Matlab software from wire-line Logs including gamma ray, density, neutron, sonic and photoelectric (PE). This method can reduce requirement of coring and reduce the costs. The area we have studied, consist of three lithoLogies, including Dolomite, shale and Anhydrite. The regression between the predicted and the real values of volume concentrations of Dolomite, shale and Anhydrite are obtained respectively, as 0.87, 0.76 and 0.90. The results show that the neural network gives a reasonable estimation for lithoLogy.

Pore pressure is one of the most important reservoir-drilling parameters and knowledge of this pressure is essential for drilling costs, well safety and prevention of potential hazards. Research has shown that experimental equations have good performance accuracy only for certain regions. Most of these experimental equations have been compiled and developed based on a limited data set. Therefore, these correlations are valid in the range of changes in the parameters of those fields and are not valid for other areas. Therefore, artificial intelligent methods have given way to empirical equations. In this study, 2827 data related to three wells from one of the oil fields located in the southwest of Iran have been used. The input variables used in this paper to predict the pore pressure include 9 variables that have been selected using the feature selection method. In this study, 4 artificial intelligence algorithms include,random forest algorithm, support vector regression algorithm, artificial neural network algorithm and decision tree algorithm have been used to predict the pore pressure. After reviewing the results, it was found that the performance accuracy of the decision tree algorithm is higher than the other three algorithms (performance accuracy for the entire data set including R2 = 0. 9985 and RMSE = 14. 460 psi). Among the advantages of this algorithm compared to other algorithms are the best results without the need for statistical knowledge, separation of unnecessary data, short time to prepare data and reduction of relative error by finding the main node of the decision maker and analyzing it. Therefore, it can be concluded that with the development of this technique, it is possible to have high performance accuracy for a small amount of data from each field.

Total organic carbon is among the most important geochemical factors for source rock assessment. Considering the general scarcity of measured total organic carbon data in exploration area together with expensive and time consuming procedure of Rock-Eval pyrolysis, development of a new method for direct estimation of TOC parameter from well Log and seismic data is an important issue and the object of this study. In this paper, 2D seismic data and petrophysical data of the Pabdeh Formation from 4 wells of the Mansuri Oil field are used. Also ΔLog R was used to predict TOC values from petrophysical data. The calculated values were used as inputs for a Multi Attribute Analysis to find a Logical relation with seismic attributes. In this study, seismic inversion was performed based on Neural Networks Algorithm and the resulting acoustic impedance was utilized as an external attribute. Afterwards, a probabilistic neural network was trained using a set of predicting attributes derived from multiple regression. Subsequently, TOC was estimated by using seismic attributes with correlation coefficient of 75%. In the next step of study, the nonlinear Ant Colony Optimization technique was utilized as an intelligent tool to estimate and production TOC seismic section from seismic attributes. Nonlinear Ant Colony calculates weight factors for each of seismic attributes. Finally, having these weights and seismic attributes, TOC seismic section was produced.