Korhan Ayranci

The Lower Silurian Qalibah Formation is a key component of the Arabian Basin petroleum system and hosts the organic-rich Qusaiba Member, a major source rock for Paleozoic hydrocarbon reservoirs in Saudi Arabia. Despite its importance, the micro-scale sedimentological characteristics and depositional processes of the Qalibah Formation remain poorly constrained. This study presents a microfacies analysis integrated with sedimentological and ichnological observations from Qalibah Formation outcrops in the Qassim Region, Saudi Arabia. Five microfacies were identified, from bottom to top: (1) massive claystone with sparse bioturbation (BI 0–1), (2) claystone with starved ripples, (3) massive to graded siltstone, (4) massive to laminated sandstone, and (5) rippled and low-angle laminated sandstone with moderate bioturbation (BI 2–3). These microfacies record suspension-settling, sediment-laden density-flow deposition, and tractional reworking. Trace fossils, including Phycosiphon and mantle-and-swirl structures, occur mainly within event-bed siltstones, suggesting opportunistic colonization following rapid deposition. Sandier microfacies exhibit greater diversity of trace fossils, which disrupt primary sedimentary fabrics. Vertical coarsening-upward facies relationships are interpreted to represent deposition within a deltaic system evolving from offshore to distal prodelta to proximal delta-front environments. These results improve understanding of sediment transport and biogenic modification, and provide insights into heterogeneity in mud-dominated unconventional systems.

Geological cross-sections are a fundamental tool for subsurface interpretation, yet their construction remains a labor-intensive and largely manual process that relies on expert judgment and structured numerical inputs. While recent advances in artificial intelligence have enhanced specific geoscientific workflows, no existing method enables the direct generation and iterative refinement of geological cross-sections from unstructured natural language descriptions. In this paper, we present GeoSIRR 1.0 (Geological Section Interpretation, Reconstruction & Refinement), a novel modeling framework that leverages large language models (LLMs) to translate free-form geological narratives into structured, coordinate-based cross-section geometries. GeoSIRR introduces a domain-specific language (DSL) for representing geological bodies as topologically consistent polygons and integrates automated geometric and geological validation to ensure continuity, stratigraphic consistency, and structural plausibility. The framework supports both initial model generation and conversational refinement, allowing users to iteratively modify cross-sections using natural language commands while preserving existing geometry. We demonstrate the capabilities of GeoSIRR through multiple geological scenarios, including faulted sedimentary systems, intrusive bodies, and progradational deltaic sequences, and assess repeatability across multiple generation runs. Results show that GeoSIRR consistently produces geologically plausible cross-sections and effectively incorporates conceptual refinements with reduced generation time compared to initial model construction. By directly linking qualitative geological reasoning with quantitative geometric modeling, GeoSIRR provides a self-contained, dialogue-driven approach to cross-section construction that complements existing modeling tools and offers new opportunities for education, exploratory analysis, and rapid scenario development in subsurface geoscience.

Geological cross-sections are a fundamental tool for subsurface interpretation, yet their construction remains a labor-intensive and largely manual process that relies on expert judgment and structured numerical inputs. While recent advances in artificial intelligence have enhanced specific geoscientific workflows, no existing method enables the direct generation and iterative refinement of geological cross-sections from unstructured natural language descriptions. In this paper, we present GeoSIRR 1.0 (Geological Section Interpretation, Reconstruction & Refinement), a novel modeling framework that leverages large language models (LLMs) to translate free-form geological narratives into structured, coordinate-based cross-section geometries. GeoSIRR introduces a domain-specific language (DSL) for representing geological bodies as topologically consistent polygons and integrates automated geometric and geological validation to ensure continuity, stratigraphic consistency, and structural plausibility. The framework supports both initial model generation and conversational refinement, allowing users to iteratively modify cross-sections using natural language commands while preserving existing geometry. We demonstrate the capabilities of GeoSIRR through multiple geological scenarios, including faulted sedimentary systems, intrusive bodies, and progradational deltaic sequences, and assess repeatability across multiple generation runs. Results show that GeoSIRR consistently produces geologically plausible cross-sections and effectively incorporates conceptual refinements with reduced generation time compared to initial model construction. By directly linking qualitative geological reasoning with quantitative geometric modeling, GeoSIRR provides a self-contained, dialogue-driven approach to cross-section construction that complements existing modeling tools and offers new opportunities for education, exploratory analysis, and rapid scenario development in subsurface geoscience.

Geological cross-sections are a fundamental tool for subsurface interpretation, yet their construction remains a labor-intensive and largely manual process that relies on expert judgment and structured numerical inputs. While recent advances in artificial intelligence have enhanced specific geoscientific workflows, no existing method enables the direct generation and iterative refinement of geological cross-sections from unstructured natural language descriptions. In this paper, we present GeoSIRR 1.0 (Geological Section Interpretation, Reconstruction & Refinement), a novel modeling framework that leverages large language models (LLMs) to translate free-form geological narratives into structured, coordinate-based cross-section geometries. GeoSIRR introduces a domain-specific language (DSL) for representing geological bodies as topologically consistent polygons and integrates automated geometric and geological validation to ensure continuity, stratigraphic consistency, and structural plausibility. The framework supports both initial model generation and conversational refinement, allowing users to iteratively modify cross-sections using natural language commands while preserving existing geometry. We demonstrate the capabilities of GeoSIRR through multiple geological scenarios, including faulted sedimentary systems, intrusive bodies, and progradational deltaic sequences, and assess repeatability across multiple generation runs. Results show that GeoSIRR consistently produces geologically plausible cross-sections and effectively incorporates conceptual refinements with reduced generation time compared to initial model construction. By directly linking qualitative geological reasoning with quantitative geometric modeling, GeoSIRR provides a self-contained, dialogue-driven approach to cross-section construction that complements existing modeling tools and offers new opportunities for education, exploratory analysis, and rapid scenario development in subsurface geoscience.

Geological cross-sections are a fundamental tool for subsurface interpretation, yet their construction remains a labor-intensive and largely manual process that relies on expert judgment and structured numerical inputs. While recent advances in artificial intelligence have enhanced specific geoscientific workflows, no existing method enables the direct generation and iterative refinement of geological cross-sections from unstructured natural language descriptions. In this paper, we present GeoSIRR 1.0 (Geological Section Interpretation, Reconstruction & Refinement), a novel modeling framework that leverages large language models (LLMs) to translate free-form geological narratives into structured, coordinate-based cross-section geometries. GeoSIRR introduces a domain-specific language (DSL) for representing geological bodies as topologically consistent polygons and integrates automated geometric and geological validation to ensure continuity, stratigraphic consistency, and structural plausibility. The framework supports both initial model generation and conversational refinement, allowing users to iteratively modify cross-sections using natural language commands while preserving existing geometry. We demonstrate the capabilities of GeoSIRR through multiple geological scenarios, including faulted sedimentary systems, intrusive bodies, and progradational deltaic sequences, and assess repeatability across multiple generation runs. Results show that GeoSIRR consistently produces geologically plausible cross-sections and effectively incorporates conceptual refinements with reduced generation time compared to initial model construction. By directly linking qualitative geological reasoning with quantitative geometric modeling, GeoSIRR provides a self-contained, dialogue-driven approach to cross-section construction that complements existing modeling tools and offers new opportunities for education, exploratory analysis, and rapid scenario development in subsurface geoscience.

Mud-dominated reservoirs exhibit significant heterogeneity driven by depositional processes and post-depositional modification, yet the role of micro-scale bioturbation remains poorly constrained. This study focuses on the impact of diminutive trace fossils (Multina and Phycosiphon) within event beds of the Lower Silurian Qusaiba Member (Qalibah Formation, Saudi Arabia) using micro-computed tomography (micro-CT), petrography, and QEMSCAN analyses. In the studied rocks, these diminutive trace fossils form dense, shallow-tier networks restricted to the upper portions of thin event beds and constitutes at least 1% of the sediment volume. Micro-CT imaging reveals complex three-dimensional burrow architectures that disrupt primary sedimentary fabrics and generate pronounced micro-scale heterogeneity. Mineralogical analyses indicate systematic differences between burrow fills and host sediment, including redistribution of clay minerals and localized enrichment of diagenetic phases such as pyrite. Consequently, the trace fossil producers modified sediment composition through selective feeding and induced early diagenetic processes. The findings demonstrate that micro-scale bioturbation is a fundamental component of mudrock fabric, potentially exerting a first-order control on porosity, permeability, and geomechanical properties. This has important implications for unconventional reservoir characterization, where subtle textural variations can significantly influence fluid flow and reservoir performance.

Geological carbon sequestration (GCS) is a key technology for mitigating CO₂ emissions from hard-to-abate industrial sources, it has been tested in various subsurface formations including basalts, coal seams, shales, carbonate rocks, sandstones and salt formations. With carbonates and sandstones being the most widely utilized reservoirs for long-term storage. Although these formations may exhibit favorable porosity and permeability, they are typically heterogenous because of various depositional processes and diagenesis modifications. Such heterogeneity has significant impact on CO₂ injectivity, migration, and storage efficiency.Bioturbation, the reworking and modification of sediments by organisms represents an additional and often underexplored source of heterogeneity in both carbonate and sandstone reservoirs. Burrow networks can locally enhance or impede fluid flow, thereby influencing CO2 injectivity, migration behavior, and storage performance. This study investigates the role of bioturbation, represented by Thalassinoides networks, in controlling CO2 storage behavior in tight sedimentary strata, with the Upper Jurassic Hanifa Formation of Saudi Arabia serving as a representative case study.High-resolution X-ray computed tomography scans of Thalassinoides-bearing carbonate rock samples were used to capture the three-dimensional geometry and connectivity of the burrow networks. These data served as training images for multipoint statistics modeling, allowing the construction of a realistic fine-scale rock model that preserve burrow morphology and spatial continuity. To facilitate dynamic flow simulations, the model was upscaled to a coarser grid while maintaining the nature of the burrow network. In this study, three different burrow permeability values (1, 10, and 100 mD) were tested while maintaining the matrix permeability constant (0.1 mD). CO₂ injection simulations were performed using a numerical reservoir simulator, testing the three different scenarios: 1. high burrow permeability (100 mD), 2. medium burrow permeability (10 mD), and 3. low burrow permeability (1 mD).The results demonstrate that the permeability contrast between the Thalassinoides burrow network and its surrounding matrix has a major control on CO₂ plume diffusion. A high permeability contrasts promote rapid injectivity while leading to a strong channelized flow confined to the burrow networks with poor CO₂ penetration into the matrix. A medium permeability contrast allows for a balanced CO₂ flow and efficient CO2 diffusion into the matrix. A low permeability contrast results in a more homogeneous CO₂ diffusion and an improved storage efficiency due to high penetration into the rock matrix.These findings highlight the necessity of incorporating bioturbation-induced heterogeneity into GCS assessments. Explicitly accounting for ichnological assemblages can improve simulation accuracy, optimize injection strategies, and support more robust site selection for GCS projects. Similar refinements can be applied in Saudi Arabia and in analogous sedimentary settings worldwide.

The Middle to Late Triassic Jilh Formation is a mixed siliciclastic-carbonate succession with complex stratigraphy. It is recognized across the Arabian Peninsula as hosting complete petroleum systems, including source rocks, reservoirs, and seals, as well as aquifers. Despite its economic significance, research on the Jilh Formation remains limited, largely due to its internal complexity and production challenges, such as overpressure zones. Furthermore, its depositional and ichnological characteristics remain poorly constrained.This study investigates a 45-meter-thick outcrop of the Lower Jilh Formation in the Riyadh Region through an integrated sedimentological and ichnological approach. Field-based analyses were complemented by laboratory investigations, including thin section petrography and geochemical characterization.Six siliciclastic facies were identified: (1) green, massive, fissile shale with sparse bioturbation; (2) yellow to reddish, massive to faintly laminated siltstone with limited Skolithos and Arenicolites; (3) horizontally laminated very fine-grained sandstone with sparse bioturbation and diverse ichnofauna; (4) heavily bioturbated, very fine- to fine-grained sandstone dominated by monospecific ichnogenera (e.g., Taenidium, Skolithos, and Rhyzoliths); (5) current-rippled, very fine- to fine-grained sandstone with moderate bioturbation (e.g. Skolithos and Lockeia); (6) low- to high-angle cross-bedded, fine- to medium-grained sandstone devoid of bioturbation.These facies represent an estuarine to fluvial system, including channels, point bars, proximal estuarine settings, and a central estuarine basin. The dominance of monospecific ichnofacies, low bioturbation intensities, and absence of fully marine trace fossils suggest environmentally stressed conditions, likely related to salinity fluctuations, rapid sedimentation, and intermittent subaerial exposure.Geochemical analysis of the outcrop reveals distinct vertical variations in elemental concentrations, delineating three distinct geochemical zones: from bottom to top (1 moderate Si and Al concentrations, indicative of mixed siliciclastic input, with minor peaks in Ca and Mg suggesting intermittent carbonate influence; (2) high Si content accompanied by a sharp decline in Al, Ca, and Mg, reflecting a dominance of coarse, quartz-rich terrigenous sediment; and (3) moderate Si and Al values with localized increases in Ca and Mg, pointing to a return of mixed siliciclastic–carbonate deposition. These geochemical trends can further be utilized to interpret paleoproxies, such as weathering intensity, paleoprovenance and fluctuating depositional conditions within the Jilh Formation.Despite evidence supporting a fine-grained fluvial system, lateral facies correlation remains challenging due to the erosional nature of thick channel deposits and frequent pinch-outs of sedimentary units. Future studies should include multiple outcrop descriptions to address these limitations. Nonetheless, this study contributes to a deeper understanding of the heterogeneity and paleoenvironmental characteristics of the Lower Jilh Formation, offering valuable insights for improved reservoir characterization.