Optimization of hydraulic fracturing in a mature Algerian oilfield: A case study

Thesis Abstract

This study addresses the declining production and suboptimal hydraulic fracturing efficiency observed in a mature oilfield in Algeria, where reservoir heterogeneity, proppant placement challenges, and operational constraints contribute to diminishing shale gas and tight oil gains. The aim is to optimize fracturing operations to enhance hydrocarbon recovery, reduce non-productive time, and improve economic performance through an integrated, field-scale assessment. Specific objectives include (i) characterizing reservoir heterogeneity and natural fractures using core analysis, well logs, and microseismic data; (ii) developing a predictive fracture design framework that links geomechanical properties to fracture geometry and hydraulics; (iii) evaluating fracturing-fluid optimization, proppant scheduling, and pump-rate strategies using a data-driven approach; (iv) implementing a decision-support model for staging, perforation clustering, and well-spacing adjustments; and (v) validating optimization strategies through retrospective production data and forward-looking simulations. The methodology employs a mixed-methods design anchored in both empirical field data and numerical modeling. The population comprises 12 mature wells across the field with historical hydraulic fracturing campaigns and complete production and intervention records from 2016–2024. A purposive sampling approach selects eight wells with comprehensive geomechanical and production datasets for in-depth analysis, while four complementary wells serve as control cases for validation. Data collection methods include (a) petrophysical and core analysis to determine minimum principal stress, Young’s modulus, fracture toughness, and pore pressure; (b) integration of microseismic monitoring, wall-coverage fiber-optic sensing, and production logs to map fracture networks; (c) operational data from fracturing jobs (fluid volumes, proppant type and concentration, pump rates, slurry design, and proppant crush resistance); (d) production metrics (bottom-hole pressure, gas/oil ratio, water cut, cumulative production) and cost records; and (e) economic inputs (oil price scenarios, operating costs, and discount rates). The analysis combines quantitative and qualitative techniques (i) multivariate regression and machine-learning models (random forests and gradient boosting) to relate rock mechanics and fracturing inputs to achieved fracture half-length, conductivity, and stimulated rock volume; (ii) geomechanical-porous flow simulations (ELF/FracSim) for scenario-based optimization of proppant placement and fluid system design; (iii) design of experiments to test alternative fracturing-fluid chemistries and proppant alternatives under field constraints; and (iv) thematic analysis of operational constraints and learnings from intervention teams to inform practical recommendations. Model validation uses a split-sample approach, with training on 60% of wells and testing on the remaining 40%, and cross-validation across different stimulation campaigns. Expected findings include improved correlation between geomechanical properties and fracture conductivity, quantified gains in stimulated rock volume under optimized pump rates and proppant schedules, and demonstrable reductions in non-productive time when staging and perforation strategies are aligned with fracture geometry. The study anticipates identifying optimal fluid-lubricant formulations and proppant designs that maximize fracture complexity while maintaining economic viability. Furthermore, the research is expected to develop a field-ready decision-support framework that integrates reservoir characterization, fracture design, and operational planning, thereby enabling near real-time adjustments during fracturing campaigns. The contribution to knowledge lies in bridging geomechanics, fracture design, and field operations within a mature Algerian oilfield context, providing a validated, replicable methodology for optimizing hydraulic fracturing in heterogeneous carbonate and tight sandstone reservoirs. The study will advance practical guidelines for tailoring fracturing programs to reservoir-specific properties, incorporating uncertainties in rock mechanics, fluid behavior, and operational constraints. It will also offer a scalable framework for integrating microseismic and production data into optimization workflows, with broader applicability to similar mature fields in North Africa and beyond. The main conclusion is that an integrated, data-driven optimization approach, emphasizing reservoir-specific geomechanical characterization, staged fracture design, and proppant-fluid optimization, can significantly enhance fracture conductivity and production recovery while reducing costs and downtime. Recommendations include adopting the proposed decision-support framework, investing in dense monitoring (microseismic and fiber-optic sensing), and implementing a phased field trial plan to generalize the optimization strategies across additional mature assets.