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This paper comprehensively reviews recently discussed sedimentological and petroleum geological topics on submarine-fan turbidites as the major hydrocarbon reservoir rocks of the world. After the proposals of primary basic submarine-fan models in 19?〇・, many academic and industrial survey results recognized that submarine-fan depositional configuration has much variation affected by various control factors. Although practical type classification has been proposed, "sandy radial fan type" and "muddy channel levee type" can be recognized as the two representative configuration types of submarine fans. MHyperpycnal 'f*low is a newly recognized flow mechanism to transport a certain amount of terrestrial organic materials into the deep sea territory, resulting in the formation of high TOC source and reservoir rocks of turbidites. Submarine-fan turbidites occur not only in a clastic deep-sea environment but also on a continental shelf as "shelf turbidites", in a carbonate setting as "calci-turbidites" and in a shale basin as calci- or silty turbidites, all of which possess the potential as exploration targets. Reservoir property of turbidite sandstones can be controlled by three basic factors: background, primary facies and secondary diagenesis factors. Coarser facies deposited in a channel or depositional lobe tends to demonstrate high reservoir quality primarily but easily be cemented 辻 diagenetic effect is intense. As the major trap type of turbidite sandstone reservoirs, the combination of structural and stratigraphic traps can easily be found in the world-wide oil fields. "Seismic geomorphology" using 3D seismic survey data has widely been used for detailed distribution and property analysis of submarine-fan tubidites, as a recentiy developed effective analytical method. The obtained data are provided to a geostatistical reservoir modeling as basic geologic framework constraints. To implement sedimentologically meaningful reservoir modeling, object-based modeling, multipoint-geostatistics using a training image, surface-based modeling and depositional process-based modeling are adoped for submarine-fan turbidite reservoirs. [ABSTRACT FROM AUTHOR]

This paper reviews the basic concept, workflow and methodological variation of geostatistical sediment body modeling. Geostatistics was originally developed in the mining industry during 1960'and 1970'for the purpose of mathematical estimation of high-quality lode distributions, and since then, has widely been used for multiple purposes, including petroleum exploration and production. Geostatistical sediment body modeling is a quantitative and stochastic modeling method of sediment body basically using geostatistics. The basic data for the geostatistical sediment body modeling include well petrophysics as hard data and 2D/3D seismic attribute data as soft data, and they are integrated using various geostatistical algorithms to conduct the geostatistical stochastic simulation. To obtain realistic modeling results, geological and sedimentological deterministic/qualitative model information should be provided as the constraints for the geostatistical stochastic simulation. Various geostatistical simulation methods have been developed in response to the demand for the geological/sedimentological information input, as the geological/sedimentological factors are crucial for simulating the precise facies and property distributions. In addition to the past standard method of two-point pixel-based geostatistical simulation, new methodology has been proposed after the mid 1990', including object-based modeling, multiple-point pixel-based modeling, process-aided modeling, surface-based modeling and process model-based modeling. The object-based modeling is a stochastic simulation based on a sediment body object such as a lobe and meandering channel, which size, dimension and orientation trends are set using known information. The multiple-point pixel-based modeling is a stochastic simulation using a pixel-based training image of a sediment body, and utilizes data points as hard data. The process-aided modeling is a modeling using sedimentary process algorithm and experimental results. Surface-based modeling is a simulation based on a depositional surface, which dimension is determined on the basis of known information. The depositional process-based modeling is a simulation based on the forward process model using reasonable algorithm matching actual sedimentary processes. In addition to the new methodology developments, selection of appropriate modeling workflow and methodology is crucial for successful modeling, in consideration of the purpose of the modeling, depositional system of the target, and data condition in density, quality and distribution. [ABSTRACT FROM AUTHOR]

This paper attempts to review effective methodology for evaluating geologic risks in the distributions and properties of petroleum system-related sediments, mainly focusing on reservoir/source rocks and migration carrier beds. Since geologic uncertainty may differ depending on the data conditions in the prospect area or targeted basin, appropriate sedimentologic and geologic methodology for predicting the distributions and properties should be selected for better risk assessment in each data condition case. In case the data are sparse or spatially limited in and around the prospect area, it can be effective to apply the methods examining the relative position witin the basin, sediment supply systems, and depositional systems, with practical use of present analogues, the source-to-sink theory, and numerical modeling. If three-dimensional sesimic survey data are available, then effective information can directly be obtained for risk assessment. In case the data are dense in the prospect area, the integrated evaluation of geophysical data, deterministic modeling and geostatistical modeling can be applicable for high-quality and quantitative risk assessment. In this case, deterministic geologic and sedimentologic data should be used as geologic constraints in the geostatistical modeling. Even in the sparse data case, it can be suggested that multiple combination of distribution/peroperty analysis methods provides key information for geologic uncertainty reduction and accurate risk assessment. Improvement of data density and quality can be another key point for precise risk assessment. [ABSTRACT FROM AUTHOR]