Predicting microporosity and permeability in clastic reservoirs is a challenge in reservoir quality assessment, especially in formations where direct measurements are difficult or expensive. These reservoir properties are fundamental in determining a reser voir's capacity for fluid storage and transmission, yet conventional methods for evaluating them, such as Mercury Injection Capillary Pressure (MICP) and Scanning Electron Microscopy (SEM), are resource - intensive. The aim of this study is to develop a cost - effective machine learning model to predict complex reservoir properties using readily available field data and basic laboratory analyses. A Random Forest classifier was employed, utilizing key geological parameters such as porosity, grain size distri bution, and spectral gamma - ray (SGR) measurements. An uncertainty analysis was applied to account for natural variability, expanding the dataset, and enhancing the model's robustness. The model achieved a high level of accuracy in predicting microporosity (93%) and permeability levels (88%). By using easily obtainable data, this model reduces the reliance on expensive laboratory methods, making it a valuable tool for early - stage exploration, especially in remote or offshore environments. The integration of machine learning with uncertainty analysis provides a reliable and cost - effective approach for evaluating key reservoir properties in siliciclastic formations. This model offers a practical solution to improve reservoir quality assessments, enabling more i nformed decision - making and optimizing exploration efforts.

Already attracting considerable industry interest in GeoSoftware's PowerLog petrophysical software, Python ecosystems in HampsonRussell and Jason will let experts and data scientists completely customize machine learning and reservoir characterization workflows by using extensively available Python machine learning libraries and also their own proprietary code. Python ecosystems allow users to efficiently research and test various state-of-the-art machine learning workflows for proof-of-concept or commercial projects. G&G experts and data scientists can use Ecosystem workflows pre-built by CGG or they can build their own new reservoir characterization workflows using the latest open source machine learning packages, such as Google's TensorFlow. HampsonRussell and Jason users, even those with limited expertise in machine learning or Python scripting, will now benefit from complete control over input data and analysis output. With Python ecosystems, users can process data with pre-built or client-proprietary Python scripts or Jupyter notebooks, and store input and output data in either a HampsonRussell or Jason project database or a shared directory.

Over the last two years, we have highlighted deep learning use cases in enterprise areas including genomics, large-scale business analytics, and beyond, but there are still many market areas that are still building a profile for where such approaches fit into existing workflows. Even though model training and inference might be useful, for some areas that have complex simulation-driven workflows, there are great efficiencies that could come from deep neural nets, but integrating those elements is difficult. The oil and gas industry is one area where deep learning holds promise, at least in theory. For some steps in the resource discovery workflow, deep learning could lead to faster and more accurate results for potential discovery zones. Reservoir characterization is a critical step in this discovery process and is currently a hot area for explorations into how deep learning might be applied.

This paper presents the development of a hybrid learning system based on Support Vector Machines (SVM), Adaptive Neuro-Fuzzy Inference System (ANFIS) and domain knowledge to solve prediction problem. The proposed two-stage Domain Knowledge based Fuzzy Information System (DKFIS) improves the prediction accuracy attained by ANFIS alone. The proposed framework has been implemented on a noisy and incomplete dataset acquired from a hydrocarbon field located at western part of India. Here, oil saturation has been predicted from four different well logs i.e. gamma ray, resistivity, density, and clay volume. In the first stage, depending on zero or near zero and non-zero oil saturation levels the input vector is classified into two classes (Class 0 and Class 1) using SVM. The classification results have been further fine-tuned applying expert knowledge based on the relationship among predictor variables i.e. well logs and target variable - oil saturation. Second, an ANFIS is designed to predict non-zero (Class 1) oil saturation values from predictor logs. The predicted output has been further refined based on expert knowledge. It is apparent from the experimental results that the expert intervention with qualitative judgment at each stage has rendered the prediction into the feasible and realistic ranges. The performance analysis of the prediction in terms of four performance metrics such as correlation coefficient (CC), root mean square error (RMSE), and absolute error mean (AEM), scatter index (SI) has established DKFIS as a useful tool for reservoir characterization.

This paper describes some work on automatically generating finite counterexamples in topology, and the use of counterexamples to speed up proof discovery in intermediate analysis, and gives some examples theorems where human provers are aided in proof discovery by the use of examples.

Segmentation needs to be done Aloimonos (1988), a theory is developed for determining somewhere along the way. If one is working with the twoview the motion of an observer given the flow field over a full case described in Solution Using Point Correspondences 360 degree image sphere. The method is based on the fact (above) and if the motions of the rigid bodies are that the foci of expansion and contraction for an observer small from t1 to t2, the following approach can be tried.

Schlumberger is a large, multinational corporation concerned primarily with the measurement, collection, and interpretation of data. For the past fifty years, most of the activities have been related to hydrocarbon exploration. The efficient location and production of hydrocarbons from an underground formation requires a great deal of knowledge about the formation, ranging in scale from the size and shape of the rock's pore spaces to the size and shape of the entire reservoir. Schlumberger provides its clients with two types of information: measurements, called logs, of the petrophysical properties of the rock around the borehole, such as its electrical, acoustical, and radioactive characteristics; and in terpretations of these logs in terms of geophysical properties such as porosity and mineral composition.