An Integrated Approach for Saturation Modeling Using Hydraulic Flow Units: Examples from the Upper Messinian Reservoir

The Upper Messinian reservoirs located in the Salma Field of the Nile Delta area contain variable facies. The key reservoir interval of the Abu Madi Formation was deposited in fluvial to deltaic environments. These fine-grained facies form significant reservoir heterogeneity within the reservoir intervals. The main challenges in this study are reservoir characterizing and predicting the change in reservoir water saturation (SW) with time, while reservoir production life based on the change in reservoir capillary pressure (Pc). This work applies petrophysical analysis to enable the definition and calculation of the hydrocarbon reserves within the key reservoir units. Mapping of SW away from the wellbores within geo-models represents a significant challenge. The rock types and flow unit analysis indicate that the reservoir is dominated by four hydraulic flow units. HFU#1 represents the highest flow zone indicator (FZI) value. Core analysis has been completed to better understand the relationship between SW and the reservoir capillary pressure above the fluid contact and free water level (FWL), which is used to perform saturation height function (SHF) analysis. The calculated SW values that are obtained from logs are affected by formation water resistivity (Rw) and log true resistivity (RT), which are influenced by the volume of clay content and mud salinity. This study introduces an integrated approach, including evaluation of core measurements, well log analysis covering cored and non-cored intervals, neural analysis techniques (K-mode algorithm), and permeability prediction in non-cored intervals. The empirical formula was predicted for direct calculation of dynamic SW profiles and predicted within the reservoir above the FWL based on the change in reservoir pressure

An integrated sedimentological, rock typing, image logs, and artificial neural networks analysis for reservoir quality assessment of the heterogeneous fluvial-deltaic Messinian Abu Madi reservoirs, Salma field, onshore East Nile Delta, Egypt

This study introduces an integrated evaluation of geological and geophysical data, including sedimentology, diagenetic alteration, image log analysis, core measurements, formation evaluation, and a neural analysis technique (K-mode algorithm) to characterize the upper Messinian heterogeneous reservoirs of the Salma Field, Nile Delta, Egypt. It links observed reservoir permeability and flow zone indicators (FZI) to predict reservoir quality and distribution within un-cored parts of the field. Core and image log analysis show that the Abu Madi sandstone reservoir is composed of seven clastic litho-facies deposited within fluvial to deltaic environments. The reservoir is controlled by four hydraulic flow units (HFU's) and five flow units (FU). Fluvial channel facies, tidally influenced fluvial channel facies, and uppermost parts of bayhead delta facies are dominated by clean sandstone with a low clay content (avg. 20%). These facies are characterized by the high pore-throat sizes (R35 and FZI values), indicating a pore system dominated by mega-to macro-pores. The estuarine facies is composed of mudstone, siltstone, and argillaceous sandstone, with 25% average clay content and moderate R35 and FZI values, indicating a pore system dominated by macro-to meso-pores. The heterolithic estuarine and bayhead delta facies contain abundant argillaceous-rich sandstones, with 29% average clay content and low R35 and FZI values, indicating a pore system dominated by micro-pores. A neural log technique was applied to predict FZIs and permeability in un-cored intervals. Paleocurrent analysis was conducted using image log data to guide sweet spot and reservoir quality tracking across the field. Reservoir quality is controlled by both diagenetic and depositional processes, chiefly an abundance of detrital clays, grain size, and sorting. In the Salama Field reservoirs, mineral dissolution, cement dissolution, and micro-fractures enhance the pore system, while pore-filling and grain-coating detrital clays reduce reservoir quality. These results are important as they improve the wider understanding of the Messinian Abu Madi reservoir in the wider Mediterranean region

A Common Language for Gulf War Illness (GWI) Research Studies: GWI Common Data Elements

AIMS: The Gulf War Illness programs (GWI) of the United States Department of Veteran Affairs and the Department of Defense Congressionally Directed Medical Research Program collaborated with experts to develop Common Data Elements (CDEs) to standardize and systematically collect, analyze, and share data across the (GWI) research community. MAIN METHODS: A collective working group of GWI advocates, Veterans, clinicians, and researchers convened to provide consensus on instruments, case report forms, and guidelines for GWI research. A similar initiative, supported by the National Institute of Neurologic Disorders and Stroke (NINDS) was completed for a comparative illness, Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS), and provided the foundation for this undertaking. The GWI working group divided into two sub-groups (symptoms and systems assessment). Both groups reviewed the applicability of instruments and forms recommended by the NINDS ME/CFS CDE to GWI research within specific domains and selected assessments of deployment exposures. The GWI CDE recommendations were finalized in March 2018 after soliciting public comments. KEY FINDINGS: GWI CDE recommendations are organized in 12 domains that include instruments, case report forms, and guidelines. Recommendations were categorized as core (essential), supplemental-highly recommended (essential for specified conditions, study types, or designs), supplemental (commonly collected, but not required), and exploratory (reasonable to use, but require further validation). Recommendations will continually be updated as GWI research progresses. SIGNIFICANCE: The GWI CDEs reflect the consensus recommendations of GWI research community stakeholders and will allow studies to standardize data collection, enhance data quality, and facilitate data sharing