Knowledge of the composition (mineral and fluid proportions) of rock formation lithologies is important for petrophysical and rock physics analysis. The mineralogy of a rock formation can be estimated by solving a system of linear equations that relate a class of geophysical log measurements to the petrophysical properties of known minerals and fluids. This method is useful for carbonate rocks with complex mineralogies and a wide range of other lithologies. Although this method of linear inversion for rock composition is well known, there are no interactive, open-source programs for routinely estimating rock mineralogy from standard digital geophysical wireline logs. We present an interactive open-source program, MinInversion, for constructing a balanced system of linear equations from digital geophysical logs and estimating the rock mineralogy as an inverse problem. MinInversion makes use of a library of petrophysical properties that can be easily expanded and modified by the users. MinInversion also provides several options for solving the system of linear equations and executing the linear matrix inversion including least squares, LU-decomposition and Moore-Penrose generalized inverse methods. In addition, MinInversion enables the estimation of the joint probability distributions for constituent minerals and measured porosity. The joint probability distributions are useful for revealing and analyzing depositional or diagenetic composition trends that affect porosity. The program introduces ease and flexibility to the problems of rock formation composition analysis and the study of the effects of rock composition on porosity.

We have developed a support vector machine (SVM) method that relies on core-measured data as well as gamma-ray, deep resistivity, sonic, and density wireline well-log data in identifying thermally mature total organic carbon (TOC)-rich layers at depth intervals with missing geochemical data in unconventional resource plays. We first test the SVM method using the Duvernay Shale Formation data. The SVM method successfully classifies the TOC data set into TOC-rich and TOC-poor classes and the [ ] data set into thermally mature and thermally immature classes when the optimal features are selected. To further test the SVM approach, we generate depth-separated training and test data sets from a well in the Duvernay Shale Formation and successfully use the approach to identify thermally mature TOC-rich intervals. We also examine the successful cross basin application of the SVM approach in predicting TOC using data from the Barnett and Duvernay Shale Formations as the training and test data sets, respectively.

This study presents a comparative analysis of CO₂-EOR and water flooding scenarios to optimize oil recovery in a geologically heterogeneous reservoir with a dome structure and partial aquifer support. Using production data from twelve production and three monitoring wells, a dynamic reservoir model was built and successfully history-matched with a 1% deviation from actual field data. Three main recovery methods were evaluated: water flooding, continuous CO₂ injection, and water-alternating-gas (WAG) injection. Water flooding resulted in a four-fold increase from primary recovery, while continuous CO₂ injection provided up to 40% additional oil recovery compared to water flooding. WAG injection further increased recovery by 20% following water flooding. The minimum miscibility pressure (MMP) was determined using a 1D slim-tube simulation to ensure effective CO₂ performance. A sensitivity analysis on CO₂/WAG ratios (1:1, 2:1, 3:1) revealed that continuous CO₂ injection, particularly in high permeability zones, offered the most efficient recovery. An economic evaluation indicated that the optimal development strategy is 15 years of water flooding followed by 15 years of continuous CO₂ injection, resulting in a net present value (NPV) of USD 1 billion. This study highlights the benefits of CO₂-EOR for maximizing oil recovery and suggests further work on hybrid EOR techniques and carbon sequestration in depleted reservoirs.

This paper aims to evaluate the efficiency of various machine learning algorithms integrating with numerical simulations in optimizing oil production for a highly heterogeneous reservoir. An approach leveraging a machine learning workflow for reservoir characterization, history matching, sensitivity analysis, field development and optimization was proposed to accomplish the above goal. A 3D subsurface model representing studied sand-shale sequences was constructed based on geophysical and petrophysical logs, core measurements, and advanced machine learning techniques. After that, a robust sensitivity analysis and history matching process were conducted using a machine learning workflow. The most sensitive control variables were the aquifer properties, permeability heterogeneity in different directions, and water–oil contacts. The history matching results from the constructed geological model showed that the oil rate, water rate, bottom hole pressure, and average reservoir pressure were matched within a 10% deviation from the observed data. Several field development scenarios were generated using the validated model to optimize cumulative oil recovery. Different injection well placement locations, well patterns, and the possibility of converting existing oil-producing wells to water injection wells were investigated. A machine learning-based proxy model was built for the prediction of cumulative oil production and then optimized with hybrid machine learning techniques. The Artificial Neural Network (ANN) algorithm was found to provide higher field cumulative oil production compared with the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) of 3.5% and 26.5%, respectively. Following the detailed proposed machine learning-based workflow, one can effectively decide on the development strategy and apply the findings from this research to their field.

Fischer plots are a technique that is used to graph changes in accommodation in cyclic carbonate successions. They typically depict the cumulative departure from the average cycle thickness as a function of the cycle number or stratigraphic depth. Many applications of Fischer plots focus on their construction from exposed cyclic carbonate successions. No published programs allow the direct construction of Fischer plots from digital wireline well-logs or dynamic presentations of Fischer plots. Here, we introduce a program known as FischerLab, which facilitates the generation and analysis of Fischer plots. In addition to accepting interpreted stratigraphic data input, FischerLab facilitates the interpretation of digital wireline logs for the generation of Fischer plots in cycle and depth domains, as well as in a dynamic evolving cycle and relative depth domain from an easy-to-use interface. The dynamic construction facilitates the correlation of specific stratigraphic packages to parts of the accommodation cycle while simultaneously tracking the locus of the mean subsidence vector. We demonstrate the use of FischerLab on data derived from the carbonate succession outcrops of the Al-Athrun Formation, Libya, and the Glen Rose Formation, Central Texas, USA, as well as on wireline well-log data from the Western Great Bahama Bank, the Bahamas.

Numerical simulations enable us to couple multiphase flow and geochemical processes to evaluate how sequestration impacts brine chemistry and reservoir properties. This study investigates these impacts during CO₂ storage at the San Juan Basin CarbonSAFE (SJB) site. The hydrodynamic model was calibrated through history-matching, utilizing data from saltwater disposal wells to improve predictive accuracy. Core-scale simulations incorporating mineral interactions and equilibrium reactions validated the model against laboratory flow-through experiments. The calibrated geochemical model was subsequently upscaled into a field-scale 3D model of the SJB site to predict how mineral precipitation and dissolution affect reservoir properties. The results indicate that the majority of the injected CO₂ is trapped structurally, followed by residual trapping and dissolution trapping; mineral trapping was found to be negligible in this study. Although quartz and calcite precipitation occurred, the dissolution of feldspars, phyllosilicates, and clay minerals counteracted these effects, resulting in a minimal reduction in porosity—less than 0.1%. The concentration of the various ions in the brine is directly influenced by dissolution/precipitation trends. This study provides valuable insights into CO₂ sequestration’s effects on reservoir fluid dynamics, mineralogy, and rock properties in the San Juan Basin. It highlights the importance of reservoir simulation in assessing long-term CO₂ storage effectiveness, particularly focusing on geochemical interactions.