Abstracts Poor reservoir characterization poses unique problems for exploration and production when the complex variation in lithology and diagenetic history that result in heterogeneous reservoir quality are not addressed. Optimizing field development requires a level of reservoir description that adequately defines vertical and lateral variations in reservoir quality. An accurate reservoir description is a key to improved field development planning and management. This study shows how integrating petrophysics and geology helps in reservoir characterization. This is illustrated with data from Niger Delta reservoir sandstone. Introduction One of the most challenging activities in reservoir modelling is the integration of all available data. Ability to integrate all available data improves understanding of reservoir characterization in a field development project2. Subtle characteristics within a subsurface formation can reveal important features, from regional geology to detailed local reservoir properties. Since investment decisions for future field development are commonly based on future field performance predicted from static and dynamic models, their accuracy usually dictate the decision quality. Challenges to reservoir characterization areidentifying what types of reservoir heterogeneities are most relevant to fluid flows so that the right types of data can be acquired anddetermining how to build robust reservoir models with limited subsurface information, and often times typically in a short time frame3,4. This paper focuses on the integration of all available data in the characterization of a reservoir in the Niger Delta. We have chosen the X-reservoir of Abia field because it is hydrocarbon bearing and the wells are producing. This provides a good candidate for integration of geology and petrophysics for reservoir characterization. Data Availability The reservoir being studied is drained by seven wells. Available data include: Deviation data, Well surface locations, lithologic data, and property logs. Sidewall core data is available but not detailed. Regular production tests were performed on the Wells and a satisfactory historical production data, fluid samples and pressure data were also available.

Unavailability of core data in most Niger delta wells makes pertinent the need for a reliable permeability distribution. This paper accounts for how permeability was modelled in a reservoir without core data in the Niger Delta by using five empirical approaches namely, Timur, Coates, Tixier, Udegbunam and also a correlation generated from core data of a nearby field. The five permeability results from the five approaches were used in building five different 3 D geological models. Flow simulation was carried out for all the models to analyse their flow peformance. The permeability distribution from the correlation generated from the nearby field core data yielded a higher oil recovery. Introduction The permeability of a rock is one of the most important parameters neccessary for effective reservoir characterization and management. Therefore accurate knowledge of its distribution in the reservoir is critical to accurate production performance prediction. During primary depletion, areal variation of permeability influences oil recovery. Permeability measurements from cores are direct measurement of these properties. But a reservoir without core data is often associated with uncertainties as these properties have to be log derived. Permeability of a formation is affected by factors such as porosity and pore space characteristics, types, amount and distribution of clay minerals, rock matrix composition and size of matrix grains. Several authors have proposed models for permeability determination in an uncored reservoir using well logs. These models are based on correlation between permeability, porosity and irreducible water saturation. Irreducible water saturation being a function of the rock characteristics. The workflow consists of petrophysical evaluation, permeability estimation, 3D geocellular modeling, upscaling of fine grid models for flow simulation and dynamic modeling.

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