Semi-Amalgamated Thinly-Bedded Deepwater GOM Turbidite Reservoir Performance Modeled Using Object-Based Technology and Bouma Lithofacies

Abstract

Abstract Determining the relationship between well density and recovery is very difficult for a semi-amalgamated thinly-bedded turbidite system under sparse well control where individual channels cannot be mapped. The primary risks facing commercialization under such a situation include reservoir stratigraphic compartmentalization and poorer tortuosity-driven rate/time performance. In this study, these critical features were modeled stochastically using an object-based technology, which combined site specific depositional concepts with quantitative channel architectural information obtained from analog outcrops. A key parameter that significantly affected the resultant channel architecture was determined to be the net-to-gross ratio (NTG) derived from logs and core images; stochastic realizations were generated for the various NTG end-members. Flow simulations were performed at the geologic model scale to preserve the fine-scale architectural and bulk property features. It was observed that stratigraphic compartmentalization became less relevant above a NTG ratio of approximately 25% within an areal scale of about 600 acres. Reservoir tortuosity however, remained the single most important characteristic affecting the relationship between well density and economic recovery. Introduction The field to be developed is located in the Eastern Gulf of Mexico in a water depth of 3800 feet. The reservoir under study lies at an average depth of 19,000' TVDSS with initial reservoir pressure and temperature of 13,800 psia and 190 degrees F, respectively. The reservoir is defined by two well penetrations approximately three (3) miles apart and a high-quality 3-D seismic survey (Fig. 1). The reservoir is interpreted as a semi-amalgamated turbidite system composed of multiple sands separated by shale beds deposited in an inter-salt basin. Based on log character, the 165' gross interval (Fig. 2) can be subdivided into two distinct depositional facies that mark a significant change in reservoir rock quality: high-energy massive sands and low-energy thinly-bedded sands. Areal extent and spatial distribution of these facies cannot be individually mapped using either seismic or log correlations. Structurally, the reservoir is confined to the east and west by two salt masses, which induce relatively high reservoir dips. The fluid is modeled as a slightly under-saturated rich-gas, retrograde-condensate system, and the reservoir is expected to deplete under pressure depletion drive. Given the limited information, greater reliance had to be placed on concept-driven tools to describe the various geologic and fluid flow features. For example, object-based stochastic modeling was used to develop the channel architecture which integrated site specific depositional concepts with observations from analog outcrops. Channels within each of the sub-environments were given unique geometries and then filled with bulk properties derived from Bouma lithofacies as studied in core. For flow simulations, a 3-D numerical segment model was used at the geologic model cell dimension (200 × 200 × 1 feet) to preserve the complex channel architectural features. Although the two primary sources of uncertainty for this reservoir are channel architecture and fluid compositional gradation, this paper will focus only on the first source. Specifically, the focus will be on the architectural features of the semi-amalgamated thinly-bedded depositional facies, such as:what controls these features;what are some of the end-members;what is the relationship between well density and recovery; andother related issues. P. 443