Integrated evaluation and optimization of acid fracturing effectiveness in carbonate reservoirs: Experimental insights and field validation. - Peng Lu; Songlei Li; Jinsong Liu; Nan Yuan; Heng Liu (PLoS ONE, 2025)
The development of carbonate reservoirs is confronted with significant challenges, including pronounced heterogeneity and suboptimal porosity-permeability characteristics. These challenges are particularly acute in low-porosity, low-permeability complex media, where acid fracturing often exhibits limited effectiveness. To address these issues, this study systematically evaluates post-acid fracturing effectiveness in carbonate reservoirs, aiming to provide robust scientific and technical guidance for stimulation optimization. A comprehensive multi-method assessment framework was employed, integrating laboratory experiments, operational curve analysis, real-time fracture monitoring, and production logging. Laboratory investigations quantified the reaction kinetics of diverse acid systems and the conductivity of acid-etched fractures. Results revealed that channel-like acid-etched fractures achieved a conductivity range of 120-150 μm²·cm, which is 3-5 times higher than that of smooth-walled core samples (25-40 μm²·cm). Field validation using Well A1 demonstrated strong correlations (R² = 0.85) between operational pressure fluctuations and natural fracture density. Viscosity-contrast fluid systems were shown to enhance conductivity by 40%-60% through selective etching mechanisms. The study's key innovation lies in identifying conductivity-governing factors through an integrated experimental-modeling approach, establishing criteria for optimal etching pattern development. The framework is further enriched by introducing dynamic conductivity metrics and heterogeneity indices, which deepen theoretical understanding. Practically, the study delivers actionable protocols, including: viscosity-difference fluid design, real-time pressure diagnostics for fracture network characterization, and multi-scale conductivity prediction models. These findings underscore the critical importance of post-stimulation evaluation as being on par with treatment design in carbonate reservoirs. Implementation of the proposed methodology has increased stimulation success rates from 55% to 82% in pilot fields, with sustained production gains exceeding 35%. Future work should focus on developing intelligent evaluation systems leveraging machine learning to further improve prediction accuracy and operational efficiency.