Saturation height modelling is a critical input into 3-D based resource volume estimates. Calibrating saturation height models with in-field core data, particularly from the reservoir of interest, helps to reduce the uncertainties in reservoir saturation estimates. Due to paucity of Core data in most fields; Saturation height modelling is usually done with wireline logs only, using analytical equations. These equations sometimes do not have unique solutions with the same set of log data. The accuracy of the derived solutions is further reduced when these fits are derived without calibration to core data. Some of the log-only model-fits may also not have physical meaning even when there are good visual match with saturation profile at well points; thus failing when exported into a reservoir simulator. It is therefore pertinent to find a way of constraining the results from log-derived capillary models with fitting parameters obtained using core data from analogue fields in the absence of in-situ core data in subject field.The paper demonstrates the value in the use of analogue field core data for calibration of log-derived saturation model. ZAN field is a partially appraised field that has been identified for gas development with two well penetration that have complete suite of logs required for reliable saturation estimates and no core data. Log-based Capillary Pressure models were initially built, without any form of core calibration, with very limited success. To improve the quality of the model, capillary pressure measurements from analogue fields were used as building block for an improved capillary pressure model that was subsequently calibrated with logs from the field of interest. This resulted in an improved saturation match at well positions and also improved dynamic model initialisation.

MOT reservoir has a unique case of uncertainties as a result of data paucity being in a field where no production has occurred, and there is need to reduce the uncertainties associated with the key evaluation parameters required for making investment decisions. This paper presents how a multidisciplinary team resource was leveraged on in managing the identified uncertainties to deliver a robust development plan for the reservoir of interest. The approach deployed emphasize on integration and collaborative interpretations from the constituting disciplines in the study team. Early focus was placed on uncertainty identification, quantification and management. Iterative efforts were necessary to achieve consistency of results and preservation of physical meaning as the study moves from one domain to another. A consistent framework for quantifying the respective impacts of the identified uncertainties was developed, and realizations were constrained by the most impacting parameters to generate a probable representation of the subsurface. Subsurface development concepts were tested and suitably selected to optimize recovery using the base case realization as a control, and preliminary economic evaluations were also performed to determine the project robustness to risk and the magnitude of the investment. The experience from this work provides a reliable approach to handling the development of a green field reservoir with limited data availability. An approach to overcoming several limitations on how to predict a fit-for-purpose PVT-table, developing a representative SHM were also presented, and the success obtained further emphasize the advantage of integration in a multidisciplinary team. The results showed that the high impacting uncertainties were structure, fluid contacts, and relative permeability, and the identified uncertainties were managed by building realizations to adequately capture the possible outcomes, and the preliminary project economic evaluations suggests that the project would be viable even for the Low-Case outcomes, hence adding value to the company portfolio.

The importance of multi-discipline integration in the various phases of hydrocarbon exploitation cannot be over-emphasized. In the past, the various subsurface disciplines, within the oil and gas industry, worked in silo-like organizations which often results in a sub-optimal understanding/evaluation of the subsurface data. However, in recent times, much has been done and written on multi-disciplinary integration and its benefits particularly with respect to subsurface studies. The Zed field, which is the subject of this paper, is a predominantly gas bearing partially appraised field. The field is composed of a series of stacked sandstone reservoirs located in the Niger-Delta Region of Nigeria. Given the limited subsurface data available within the hydrocarbon-bearing areas of the field (only 2 of the 6 wells in the field penetrated the hydrocarbon-bearing sections), one of the biggest challenges of developing this field remain the high level of subsurface uncertainties coupled with the potentially low economic value of further appraisal and development of the field. In order to adequately assess these uncertainties and the economic feasibility of developing the Zed field, a detailed subsurface study involving a full re-evaluation of all potential hydrocarbon bearing sands penetrated by the wells was required. The study, which kicked off with a comprehensive integrated multi-discipline data review and quicklook evaluation, resulted in the identification of two additional reservoirs previously considered too marginal to contain substantial hydrocarbon. This paper details how the systematic, multi-discipline data integration and review of these two reservoirs helped in the identification and determination of higher hydrocarbon volumes in these reservoirs; and how this has helped in improving the economic value of the Zed field development project.