Carbonate reservoirs have emerged as key targets for hydrocarbon development owing to their substantial reserves and widespread distribution. During depressurization, gas bubbles form as the reservoir pressure declines below the saturation threshold. The emergence of a gas phase introduces additional flow resistance, further hindering reservoir development. This study thoroughly investigates the mechanisms of additional flow resistance induced by gas bubbles during crude oil degassing, using a combination of experimental and theoretical approaches. Experimental results reveal that gas bubble accumulation in narrow pores can obstruct flow paths, significantly increasing resistance and diminishing oil mobility. Microscopic visualization experiments further confirm the presence of gas bubbles in various pore structures, where they form a discontinuous phase in smaller pores. This process not only exacerbates reservoir heterogeneity but also impedes the efficient development of carbonate systems. Moreover, the impact of gas bubbles on water flooding has been quantitatively assessed through a numerical simulation approach. Simulation results indicate that with increased degassing, fracture relative conductivity is enhanced, leading the water phase to preferentially flow through fractures rather than the matrix. Incorporating degassing-induced resistance leads to earlier water breakthrough and exacerbates water channeling phenomena. Additionally, cumulative oil production declines as gas bubble-induced resistance increases, further validating the influence of degassing on reservoir dynamics. This study offers both theoretical foundations and practical insights for improving recovery in carbonate reservoirs, proposing innovative strategies for more efficient development.
During plunger lift, gas bypass and liquid fallback may coexist in the extremely narrow annulus between the plunger and tubing, forming a local gas-liquid countercurrent flow affected by confinement, moving-boundary shear, and interfacial slip. Conventional drift-flux closures may therefore have limited transferability. In this study, 59 vertical air-water experiments were used to reconstruct the equivalent distribution parameter C-0 and drift velocity V-d for this local flow region. The transferability of four representative correlations-Zuber-Findlay, Gomez, Bhagwat-Ghajar, and Liu-was first evaluated. A stepwise inverse-calculation strategy was then adopted to identify the dominant variable of C-0 before reconstructing V-d using the recommended C-0. Results show that the existing correlations generally overpredict gas holdup. The reconstructed C-0 is mainly governed by gas holdup alpha(g), whereas the plunger velocity ratio Pi(p) provides only a secondary correction. In contrast, Pi(p) gives a more stable correction to the dimensionless drift velocity Y, indicating that plunger motion mainly modifies gas-liquid relative slip rather than cross-sectional distribution. For the present database, the reconstructed closure yielded mean absolute percentage errors (MAPEs) of 0.676% in back-substitution validation and 24.762% in implicit forward prediction, which were lower than those of the tested correlations. The proposed closure supports gas-holdup estimation, plunger leakage analysis, and local bypass-flow evaluation under fixed-geometry, vertical, laboratory air-pure-water, extremely narrow annular countercurrent-flow conditions, and should not be regarded as a general correlation for all downhole gas-liquid systems.
Improving drilling efficiency remains a central objective in petroleum operations due to its direct impact on time and cost. In this context, data-driven approaches have gained increasing attention for their ability to capture complex, nonlinear relationships between operational parameters and drilling performance. This study develops a predictive framework for estimating the Rate of Penetration (ROP) using supervised machine learning techniques combined with systematic data conditioning. The analysis is based on more than 11,000 measurements obtained from four horizontal wells. A rigorous preprocessing strategy was implemented to enhance data reliability, including removal of invalid entries and statistical outliers using the interquartile range method. This procedure reduced the dataset to 7297 high-quality observations. In addition, target stabilization was introduced through Exponential Moving Average smoothing (spans of 5 and 10), which reduced short-term fluctuations and improved the learnability of the ROP signal. Three tree-based regression models-Decision Tree, Random Forest, and Gradient Boosting-were evaluated under both default configurations and optimized settings. Results show that model performance is strongly influenced by data conditioning. The Random Forest model achieved the highest accuracy, with a coefficient of determination (R-2) of 0.96 and a mean squared error of 26 when trained on the Exponential Moving Average (EMA)-10 dataset. Gradient Boosting exhibited the largest improvement from hyperparameter tuning, with R-2 increasing from 0.86 to 0.95. To bridge the gap between model development and practical use, the trained models were implemented in interactive applications for real-time prediction and parameter optimization. The outcomes demonstrate that careful preprocessing and noise-aware modeling significantly enhance predictive capability. Future developments should incorporate geological information, drill-bit characteristics, and operational constraints to further strengthen the applicability of machine learning in drilling optimization.
Optimizing well spacing is an underutilized solution to low recovery rates in unconventional reservoirs, allowing for higher extraction efficiencies while avoiding complex recovery techniques and added operational presence. However, defining the optimal spacing will necessarily be influenced by inherent reservoir heterogeneities. The primary objective of this study is to understand the influence of critical reservoir parameters on optimal well spacing and associated production increase in unconventional gas reservoirs, using a representative unit of the Marcellus shale as a model system. Numerical reservoir simulation models were developed to evaluate the distinct influence of matrix permeability, matrix porosity, fracture conductivity, and pore pressure on the optimal spacing between identical wells. Cumulative production was also quantified for each configuration to understand where well spacing contributes to production loss, either due to interference (spacing too close) or unstimulated reservoir volume (spacing too wide, leaving gas that would require subsequent infilling or restimulation to obtain). For wells with a fracture half-length of 300 ft, increasing well spacing from 200 ft to 600 ft increased the 10-year cumulative production by approximately 28%. The optimal spacing, however, was found to be highly dependent on the obtained fracture half-length. It was also determined that fracture conductivity had a maximum, effective limit of 2 mD & centerdot; ft, where any further increase provided a minimal benefit of less than 5%. Understanding the effects of fracture half-lengths and reservoir parameters leads to development in shale gas reservoirs that is more efficient and generates an optimized yield of natural gas over its production lifetime.
To enhance gas extraction from low-permeability coal seams, this study introduces a synergistic liquid nitrogen (LN2) freezing-blasting method designed to mechanically precondition coal and promote fracture propagation during blasting. Coal specimens with varying moisture contents (MCs) were subjected to controlled LN2 freezing regimes, followed by uniaxial, triaxial, and tensile testing using a coal-rock triaxial creep apparatus. Subsequent blasting experiments under biaxial lateral loading elucidated fracture propagation and coalescence behaviors in frozen coal. The results show that LN2 freezing significantly alters the mechanical behavior of coal and promotes fracture initiation. Under uniaxial loading, specimens frozen for 60 min at 12% MC exhibited the strongest enhancement, with both compressive strength and elastic modulus more than doubling compared with unfrozen dry coal. This strengthening effect became more pronounced under triaxial confinement. In contrast, tensile strength associated with fracture initiation was degraded due to LN2 vaporization-induced thermal shock and frost-heave effects, reaching only 0.65 MPa at 60 min of freezing and 12% MC, a 126% reduction compared with unfrozen dry coal. Subsequent blasting experiments demonstrated that LN2 pretreatment significantly improved fracture development and connectivity, promoting the transfer of blasting energy from the near-field crushed zone to far-field interconnected fractures. Under optimal conditions (60 min of freezing and 12% MC), the brittleness index reached 24.66, surface-penetrating fractures extended up to 108.8 mm, and the crushed zone length was reduced to only 14.7% of that in unfrozen dry coal.
