--> Abstract: Characterizing Rock Type Variation with Outcrop-Based Lidar Mapping of Permo-Triassic Carbonate Strata, Jebel Akhdar, Oman, by Erwin W. Adams and Jerome A. Bellian; #90105 (2010)

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AAPG GEO 2010 Middle East
Geoscience Conference & Exhibition
Innovative Geoscience Solutions – Meeting Hydrocarbon Demand in Changing Times
March 7-10, 2010 – Manama, Bahrain

Characterizing Rock Type Variation with Outcrop-Based Lidar Mapping of Permo-Triassic Carbonate Strata, Jebel Akhdar, Oman

Erwin W. Adams1; Jerome A. Bellian2

(1) Shell International E&P, Rijswijk, Netherlands.

(2) Bureau of Economic Geology, The University of Texas at Austin, Austin, TX.

A Permo-Triassic (Wordian-Induan) succession more than 700 m thick is exposed on the Saiq Plateau in Jebel Akhdar, Oman. The outcrops provide an ideal opportunity to investigate the evolution of a carbonate depositional system equivalent to important reservoirs in the Arabian Platform, such as the Khuff Formation. On a field scale, Permo-Triassic carbonate strata in the Middle East are strongly layered and correlatable over long distances (>10 km), comprising uniform stratigraphic thicknesses and similar facies associations. Nevertheless, sedimentologic and diagenetic heterogeneity within the layers is complex creating significant lateral reservoir property variations. The diagenetic overprint, including cementation, leaching, and dolomitization can be linked strongly to the original sedimentary texture and fabric governing distinct cement and pore types that define rock types. Most of the succession exposed on Jebel Akhdar, except for the lower 120 m, is completely dolomitized. Nevertheless, well-preserved precursor fabrics can be recognized in these dolostones. To help delineate rock-type partitioning, ground-based lidar and high-resolution GPS were used to record geological observations in 3D from the cm to km scale. The data were assimilated, visualized, and modeled to create a digital outcrop model (DOM). A new method of supervised-automated feature extraction using lidar was developed and tested to identify outcrop-based rock types on the basis of geometrically corrected surface reflectivity (laser intensity) and roughness (weathering). These parameters were used to classify outcrop-based rock types and subsequently populate the DOM. Laser intensity can be used to discriminate fine- from coarse-crystalline dolostones correlative with mud versus grain-rich textures. The link between intensity and outcrop-based rock types using grain size enables us to constrain the geocellular outcrop models. These models can be used to reduce uncertainties in static reservoir models and to test the effect of heterogeneity on dynamic behavior. In addition, the data and technology can also be used to establish a virtual training dataset.