Integrated sedimentary basin monitoring: seismic, geochemical, and modeling framework for resource assessment

Thesis Abstract

The investigation addresses the challenge of integrated monitoring in sedimentary basins to improve resource assessment by linking seismic signals, geochemical signatures, and predictive models within a cohesive framework. The study aims to develop a design, implement a multimodal monitoring framework, and evaluate its effectiveness for characterizing storage and migration pathways, evaluating hydrocarbon equivalents, and quantifying uncertainties in resource estimates. Specific objectives include (i) assembling a high-resolution seismic-reflection and well-log database across a representative basin, (ii) compiling geochemical proxies (stable isotopes, trace element fingerprints, and fluid-mineral assemblages) from 250 rock and fluid samples, (iii) integrating geophysical and geochemical data into a coupled reservoir–geochemical model, and (iv) validating the framework against historical production data and independent core analyses. The methodology employs a mixed-methods research design combining quantitative seismic interpretation, geochemical analysis, and numerical simulation. The population comprises sedimentary basin segments with established hydrocarbon potential and recent geochemical anomalies, while the sample includes 40 drill sites with 320 core and cuttings samples and 60 fluid samples collected over four field campaigns. Data collection instruments encompass 3D seismic surveys, wireline logs (gamma-ray, resistivity, sonic), mass spectrometry for isotope ratios (S-34, O-18, C-13), ICP-MS for trace elements, XRD for mineralogy, and stable isotope mass balance approaches. The study also utilizes field measurements of fluid-inclusion age, carbonate-cement dating where applicable, and log-curve-based saturation analyses. Validity and reliability are ensured through standardized calibration of seismic attributes, inter-laboratory cross-checks on geochemical measurements, and blind duplicate analyses for 10% of samples. Data analysis employs a tiered approach (i) seismic interpretation using horizon-based attribute extraction and probabilistic fault-distribution modeling; (ii) geochemical data reduction via principal component analysis and hierarchical clustering to identify source signatures and diagenetic processes; (iii) development of a coupled forward-model that integrates reservoir simulation (COMSOL/TOUGHREACT with PETSc solvers) with geochemical reaction networks to simulate fluid-rock interactions under varying pressure–temperature regimes; (iv) Bayesian data assimilation to update model parameters with new data, and (v) sensitivity analyses using Sobol indices to quantify uncertainty contributions from seismic, geochemical, and model parameters. The theoretical framework draws on probabilistic risk assessment and the theory of reactive transport, anchored by concepts from the theory of basinward hydrocarbon migration and seabed-to-subsurface connectivity. Expected findings include refined delineation of migration pathways, improved geochemical fingerprinting of charge sources, and enhanced predictive capacity for reservoir quality and seal integrity under dynamic stress conditions. The study anticipates demonstrating that integrated seismic-geochemical modeling reduces resource assessment uncertainty by 25–40% compared with conventional approaches, and that Bayesian updating with new data progressively narrows posterior credible intervals for key reservoir parameters. The contribution to knowledge lies in delivering a replicable, scalable framework that couples multi-physics simulations with robust geochemical interpretation to support decision-making in resource exploration, development, and monitoring. The abstracted framework will also facilitate scenario testing for enhanced oil recovery and carbon storage risk evaluation. In conclusion, the integrated monitoring framework is expected to provide improved basin-scale diagnostics of reservoir extent, fluid history, and storage integrity, enabling practitioners to quantify risks and optimize extraction or sequestration strategies. Recommendations include adopting standardized data pipelines for real-time assimilation, expanding the sample set to include offshore analogs for cross-validation, and extending the coupled model to incorporate microbial geo-chemistry for low-temperature basins to broaden applicability.