Adesina Fadairo

Accurate prediction of geomechanical properties is important for reliable hydrocarbon reservoir exploration and development, but conventional well logging and core analysis can be costly, time-consuming, and limited by incomplete sonic-log coverage. This study develops a two-stage hybrid framework that integrates ensemble machine learning with empirical rock-physics relationships to predict geomechanical properties from readily available conventional well logs. Deep resistivity (AT90), gamma ray (GR), caliper (HCAL), neutron porosity (NPHI), and bulk density (RHOZ) logs were used as input features for model training and prediction of compressional-wave transit time (DTCO) and shear-wave transit time (DTSM). Four ensemble learning algorithms—Random Forest, Light Gradient Boosting Machine, Extra Trees, and Extreme Gradient Boosting—were evaluated, and the predicted sonic responses were subsequently incorporated into established rock-physics equations to estimate Poisson’s ratio, Young’s modulus, and shear modulus. SHapley Additive exPlanations (SHAP) were further employed to quantify feature contributions and interpret the relationship between model predictions and petrophysical controls. Extra Trees produced the best predictions, with coefficients of determination (R-squared) of 0.97 for both targets; root mean squared error and mean absolute error were 10 and 6.1 for DTSM and 3.7 and 2.1 for DTCO, respectively. Measured DTSM showed poor agreement with the Castagna correlation, whereas elastic properties calculated from machine-learning predictions agreed well with those derived from measured logs. The scientific contribution is an interpretable, formation-specific framework that connects conventional-log prediction of missing sonic curves to physics-based estimation of multiple elastic properties, providing a practical basis for reservoir characterization where complete sonic coverage is unavailable.

Carbon capture and storage (CCS) is a critical pathway for achieving net-zero emissions; however, maintaining long-term wellbore cement integrity under CO₂-rich conditions remains a major technical challenge. Conventional Class G cement is highly vulnerable to carbonation–dissolution reactions, which increase porosity and permeability, weaken mechanical strength, and ultimately compromise zonal isolation. These degradation processes create potential leakage pathways that threaten storage security and reduce CO₂ storage efficiency. This study presents a systematic optimization of Class G cement using fly ash (FA) and waste-derived eggshell powder (ESP) as supplementary cementitious materials. The objectives are to develop CO₂-resistant formulations, quantitatively evaluate geomechanical, petrophysical, and geochemical performance under simulated downhole conditions, and identify the microstructural mechanisms responsible for enhanced durability. A novel formulation strategy is introduced, leveraging synergistic calcium–silica interactions between FA and ESP to fundamentally redesign the pore structure. Beyond conventional pore size reduction, this approach promotes pore network disconnection as the dominant sealing mechanism while simultaneously enabling a self-healing (autonomous sealing) response under CO₂ exposure. The presence of reactive calcium phases from ESP enhances carbonate precipitation, allowing microcracks and pore throats to be progressively sealed through in situ mineralization. Cement systems [base cement (BS), FA/ESP (75%/25%), and FA/ESP (50%/50%)] were subjected to high-pressure, high-temperature aging (2000 psi, 170 °C) in CO₂-saturated brine for up to 60 days. Comprehensive characterization was performed using Nuclear Magnetic Resonance, ultrasonic wave propagation (Auto-Lab 1500), X-ray diffraction, and Scanning Electron Microscopy. The FA/ESP (75%/25%) formulation exhibited superior performance, achieving a 75% reduction in permeability (0.01125 mD vs 0.045 mD for base cement) and an 82.22% decrease in porosity, while base cement showed a 56.7% increase. Mechanical properties improved significantly, with increases of 19.5% in Young’s modulus and 19.3% in Poisson’s ratio, indicating enhanced structural resilience. Mineralogical analysis revealed progressive calcite formation (46.4% to 56.5%), confirming active CO₂ mineralization that transforms chemical degradation into a strengthening mechanism. These findings directly enhance wellbore integrity and CO₂ storage efficiency by reducing permeability, minimizing leakage risk, and improving long-term containment. Pore network disconnection and improved mechanical strength ensure sustained zonal isolation, while CO₂-driven mineralization enables self-sealing through calcite precipitation within pores and microfractures. The novel integration of industrial byproducts and agricultural waste offers a sustainable, cost-effective solution, establishing a new paradigm for durable, self-healing cement systems in geological carbon storage.

Summary: Fishbone drilling has gained significant attention globally, with numerous applications and successes in many oil and gas fields. This technology offers a more environmentally friendly alternative to fracturing, reducing induced seismic activities in critically stressed regions. In addition, fishbone wells increase production, connect natural fractures, and bypass damaged zones. However, despite its benefits, few models accurately predict production rates from fishbone wells, hindering informed technical and economic decisions. Existing models are often complex and difficult to apply, especially in new fields lacking historical production data. With this study, we present a simplified mathematical model, building on previous work from the literature; ribholes are assumed to be a conduit inclined at an angle θ to the horizontal in the development of the model. The analytical model combines flow in the matrix with the flow in the ribhole considering pressure drop due to friction and accumulation along the ribhole sections. The governing equations and the solution procedures are included in the work to enable readers to reproduce all results. The model was validated by applying it to the multilateral stimulation technology (MST) in the Austin Chalk carbonate reservoir in Texas, USA. A reasonable agreement was observed between the field measurement and a commercial numerical simulator (CMG, Computer Modelling Group, Calgary, Alberta, Canada). The present model outperforms previous models, showing superior accuracy with a minimal prediction error. The excellent result of the new model is attributed to the included pressure drop terms in its development that were initially ignored by previous authors. The sensitivity analysis of the present model shows that the angle of inclination and the number and size of the ribholes are the major variables that influence the performance of fishbone wells, while the length of the ribholes has the least effect on production. Finally, the present model will ensure that both technical and economic decisions are taken within the shortest time period. This model can be extended for predicting production from fishbone drilling in geothermal reservoirs.

Zwitterionic surfactants are a promising option for application in harsh reservoir conditions due to their exceptional stability, compatibility, and interfacial activity. However, surfactant adsorption remains a significant concern. This study investigates the adsorption behavior of zwitterionic surfactants was studied on complex Middle Bakken minerals under high-salinity (total dissolved solids (TDS) = 29 wt%) and high-temperature (90 °C) conditions using the spectrophotometric method. The adsorbents were prepared by grinding Bakken core plugs using a ball mill and sifting them through 40 μm mesh sieves to ensure uniform particle size distribution. The results showed that the Langmuir adsorption model accurately describes the adsorption isotherms of zwitterionic surfactants. The impact of salinity on the zwitterionic surfactants adsorption varied depending on the presence of acidic and/or basic groups in the surfactants. Using Bakken formation brine instead of brine solutions with 2% TDS resulted in a decrease in adsorption of approximately 1.06 ± 0.02 mg/g for CG3 and 0.3 ± 0.04 mg/g for both CD2 and ME1. This reduction was observed in betaine-type zwitterionic surfactants with −COO− functional groups that may gain protons, compared to their adsorption capacities in the 2% TDS brine (2.35 mg/g, 2.1 mg/g, and 1.89 mg/g, respectively). This study provides critical insights into the behavior of interfacial tension (IFT) between crude oil and surfactant solutions, which is vital for optimizing enhanced oil recovery (EOR) processes. The findings underline the importance of surfactant concentration and adsorption characteristics, offering valuable guidelines for practical applications in petroleum reservoir management. Overall, zwitterionic surfactants exhibit higher adsorption on Bakken minerals regardless of the salinity condition.

Weighting materials such as barite and ilmenite are crucial for controlling fluid density during deep or ultra-deep drilling operations. However, sagging poses significant challenges, especially in highly deviated high-pressure and high-temperature (HP/HT) wells. This leads to inadequate well control, wellbore instability, and variations in hydrostatic pressure in extended-reach wells. Given the challenges of experimental research, reliable prediction models are imperative for evaluating the interaction between the ratio of anti-sagging additives, temperature, and wellbore inclination on sag factor (SF). This research presents statistical-based empirical models for predicting the SF at various wellbore inclinations (0°, 30°, 45°, 60°, 70°, 80°, and 90°) and assessing the influence of fly ash on the SF. The regression equations, developed using the Response Surface Methodology in Minitab 18 software, show high reliability, with R² values approaching unity. Contour and surface response plots provide a clear understanding of the variable interactions. The analysis reveals that sagging is most severe at 60° to 65° inclination. At 400 °F and 60° inclination, adding 4 lb/bbl of fly ash reduces sagging in barite and ilmenite-densified fluid by 63.9% and 63.1%, respectively. Model validation shows high accuracy, with percentage errors below 3%. This study offers valuable insights for optimizing drilling fluid formulations in HP/HT well environments.