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.

Understanding fluid dynamics is key in optimal well and reservoir management practices in brown fields. This is more apparent in strong water drive reservoirs where water shut-off (WSO), zone change, recompletion and work-over drilling are key well intervention activities usually carried out to optimize the production from these fields. Geologically, the structural architecture of these reservoirs makes it difficult to have uniform fluid movement with time as they are produced. In addition, the interpretation of Carbon/Oxygen (C/O) ratios as derived from Pulse Neutron Spectroscopy (PNS) logs in terms of hydrocarbon saturation is such that, a lot of variables can influence the outcome of the result. Water leg calibration, type of completion, and open hole log interpretation are some of the factors that can affect C/O interpretations negatively. PNS logs when combined with extensive subsurface data integration can become very useful in understanding fluid dynamics and optimization of intervention activities. This has been demonstrated recently in the X Field of SPDC.The two case studies in this paper demonstrate how PNS data and subsurface integration has helped resolve both non-uniform flushing as well as interpretation problems usually associated with PNS logs. Significant cost savings of ca. USD0.5 mln and ca. 2000 bopb have been realized from optimizing the planned well intervention activities on these cases. The result and observation from the study is already being applied to de-risk other similar opportunities in the portfolio while still exploring further understanding to further improve the gains.

Permeability is one of the most important parameters of reservoir rocks; it defines the capacity of rocks to transmit fluids in pore spaces. Permeability prediction is of extreme importance in deciding the field development strategy for green reservoirs. The reservoir rocks are made up of grains, cement and pore network. The pore network is made up of larger spaces, referred to as pores, which are connected by small spaces referred to as throats. The pore spaces control the amount of porosity, while the pore throats control the movement of fluids and the quantity of rock permeability. Generally, the sources of permeability measurements in green field are from core data, well test data and Nuclear Magnetic Resonance (NMR) data. However, core information, well test information and NMR information are usually very limited due to high cost of acquisition making justification usually difficult. The consequence is that we have very low ratio of cored to the total reservoirs in the Niger Delta. This paper discusses a methodology for accurately estimating permeability using analogue fields/reservoirs core data in green reservoirs. The main factors to consider in choosing a suitable analogue includes Facies classification, relative depth of the reservoirs, average porosity and histogram of the Gamma ray values between the subject and analogue reservoirs. This selection is usually an integrated effort between the teams Geologist and Petrophysicist. In this study, two fields were selected where permeability prediction was based on analogue core data. A robust Niger delta wide analogue selection process was applied first to identify the analogue field where core data exists. After selection of the analogue field, facies-wise poroperm transform was built. This poroperm transforms were then validated in one of the fields where real core measurements were available post study. This blind test with real core permeability data indicated an excellent match with analogue based permeability model. In the other field, the analogue based permeability was validated against NMR and mobility data acquired in some of the reservoirs. This workflow establishes the robustness of using existing analogue data to reduce the subsurface uncertainty and justify an integrated workflow of estimating permeability in the green field rather than acquiring a new data to support development decision.