Delineation of reservoir sand quality by integrating leading edge technology well log data and calibrating to core data

Abstract

This study targets effective methods of identifying those sand sequences within the shallow clastics of the Late MiocenelPliocene which are most productive. This example comes from offshore Bintulu within the South China Sea. These sands are characteristically unconsolidated, generally fine grained, and greenish in color resulting from their glauconitic content. The reservoir sands are deposited in a shallow marine (possibly shoreface) depositional environment. The environment is interpreted from a bioturbated sand facies and a laminated sand facies. The bioturbated sand has shell fragments and burrowing which is indicative of a shallow water depth. Due to the reduced reservoir quality associated with bioturbated sands, it is advantageous to be capable of differentiating these sand sequences from fine grained or laminated sands. Additionally, a method of determining mobile fluid within the porosity is essential as these sands are generally quite high in irreducible capillary bound water. An effective method of identifying permeability is also required in order to identify good quality reservoir sands. Magnetic Resonance log data was acquired on this well with the objective of identifying capillary bound bulk volume irreducible water in addition to acquiring a continuous permeability. This permeability is computed from t h e relationship between bulk volume irreducible and movable fluid. These parameters were to be calibrated from capillary pressures and poroperm data acquired from conventional cores. A further objective was to establish calibrations such that real time acquisition of accurate permeability and BVI would be available on further wells. Simultaneous Acoustic and Resistivity image data was also acquired on this well with the intention of using this textural information to predict lithofacies. This can be achieved by using a multivariate histogram database populated with dual image attribute electrofacies, conventional log data, magnetic resonance log data and core lithofacies. Interrogation of the database with all but core lithofacies enables prediction of core lithofacies. Historically this technique has been used with marginal success in differentiating facies types, but with the inclusion of dual image data (resistivity and acoustic image), results prove so improved as to be capable of predicting with very high confidence core lithofacies. Calibrated reservoir quality indicators such as permeability, lithofacies, bulk volume irreducible and porosity with an accurate quantification of hydrocarbon results in the ability to selectively perforate these reservoirs to produce at maximum optimal rates with minimal surface handling equipment costs. The ability to calibrate this acquired data allows for real time acquisition of permeability and BVI.