Formation damage in drilling comes from drilling fluid invasion due to high differential pressure between a wellbore and the formation. This mechanism happens with fracture fluid invasion of multi-fractured horizontal wells in tight formations. Some multi-fractured wells show production rates and cumulative productions far lower than expected. Those damaged wells may sustain further impact such as well shutting due to unexpected events such as the COVID-19 outbreak and then experience a further reduction in cumulative production. This paper focuses on the root causes of formation damage of fractured wells and provides possible solutions to improve production. A simulation study was conducted using Computer Modelling Group software to simulate formation damage due to fracture fluid invasion and well shut-in. Simulation results revealed that the decrease in cumulative hydrocarbon production due to leak-off and shut-in of the simulated well could range from 20 to 41%, depending on different conditions. The results showed that the main causes are high critical water saturation of tight formations, low drawdown, and low residual proppant permeability under formation closure stress. The sensitivity analysis suggests two feasible solutions to mitigate formation damage: optimizing drawdown during production and optimized proppant pack permeability of the hydraulic fracturing process. Optimizing pressure drawdown is effective in fixing leak-off damage, but it does not mitigate shut-in damage. Formation damage due to shut-in should be prevented in advance by using an appropriate proppant permeability. These key findings enhance productivity and improve the economics of tight gas and shale oil formations.

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.

Hydraulic fracturing has enabled production from unconventional reservoirs in the U.S., but production rates often decline sharply, limiting recovery factors to under 10%. This study proposes an optimization workflow for the CO₂ huff-n-puff process for multistage-fractured horizontal wells in the Wolfcamp A formation in the Delaware Basin. The potential for enhanced oil recovery and CO₂ sequestration simultaneously was addressed using a coupled geomechanics–reservoir simulation. Geomechanical properties were derived from a 1D mechanical earth model and integrated into reservoir simulation to replicate hydraulic fracture geometries. The fracture model was validated using a robust production history matching. A fluid phase behavior analysis refined the equation of state, and 1D slim tube simulations determined a minimum miscibility pressure of 4300 psi for CO₂ injection. After the primary production phase, various CO₂ injection rates were tested from 1 to 25 MMSCFD/well, resulting in incremental oil recovery ranging from 6.3% to 69.3%. Different injection, soaking and production cycles were analyzed to determine the ideal operating condition. The optimal scenario improved cumulative oil recovery by 68.8% while keeping the highest CO₂ storage efficiency. The simulation approach proposed by this study provides a comprehensive and systematic workflow for evaluating and optimizing CO₂ huff-n-puff in hydraulically fractured wells, enhancing the recovery factor of unconventional 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.

Electrofacies are log-related signatures that reflect specific physical and compositional characteristics of rock units. The concept was developed to encapsulate a collection of recorded well-log responses, enabling the characterization and differentiation of one rock unit from another. The analysis of the lateral and vertical distribution of electrofacies is crucial for understanding reservoir properties; however, well-log analysis can be labor-intensive, time-consuming, and prone to inaccuracies due to the subjective nature of the process. In addition, there is no unique way of reliably classifying logs or deriving electrofacies due to the varying accuracy of different methods. In this study, we develop a workflow that mitigates the variability in results produced by different clustering algorithms using a committee machine. Using several unsupervised machine learning methods, including k-means, k-median, hierarchical clustering, spectral clustering, and the Gaussian mixture model, we predict electrofacies from wireline well log data and generate their 3D vertical and lateral distributions and inferred geological properties. The results from the different methods are used to constitute a committee machine, which is then used to implement electrofacies-guided well placement. 3D distributed petrophysical properties are also computed from core-calibrated porosity and permeability data for reservoir simulation. The results indicate that wells producing from a specific electrofacies, as predicted by the committee machine, have significantly better production than wells producing from other electrofacies. This proposed detailed machine learning workflow allows for strategic decision-making in development and the practical application of these findings for improved oil recovery.

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.