Core Flood analysis is one of the processes that can be deployed towards investigating the property changes associated with the reservoir post polymer flooding. The core flooding system is a test to determine rock permeability and how a core sample will interact with various fluids. The core sample is placed in a rock core holder. Once the core is inserted in the core holder, the outer surface is pressurized to simulate the same reservoir pressure created by the overburden in the reservoir. A test fluid is subsequently passed through the core and the flow rates and pressure drops across the system is measured. From this data, various parameters are measured including unsteady state liquid relative permeability and single-phase permeability, EOR tests, Residual Resistance Factor (RRF) and Resistance Factor (RF). The mobility of water to the mobility of a polymer solution under the same conditions is defined as the Resistance Factor (RF) (Jennings et al. 1971)1. This is a good index in the determination of the success of a good polymer flood (see the equation below). Kw and Kp are water and polymer relative permeabilities and µw and µp are water and polymer viscosities, respectively. RF = KwKpμpμw Residual Resistance Factor (RRF) is another useful parameter which is defined as the ratio of the initial water mobility to the water solution mobility after polymer flooding (Jennings et al. 1971)1: where wi indicates the water permeability or viscosity at initial condition (before polymer injection) and wa is the water solution permeability and viscosity after polymer flooding. The RRF is a quantitative indication of the reduction of water mobility which controls the water fingering process due to water injection after polymer flooding (Jennings et al. 1971)1.RRF = KwiKwaμpaμwi This study investigated the RF and RRF associated with a Niger Delta reservoir post Polymer Alternating Gas (PAG) flood which was subsequently instrumental in the selection of an optimal (PAG) slug model. Biopolymer (Xanthan) and Synthetic polymer (Polyacrylamide) were used as the base fluids. Polyacrylamide which is a copolymer of acrylic acid/acrylamide and Xanthan has been used extensively in enhanced oil recovery (EOR) processes. High molecular weight polyacrylamide strongly increases the water viscosity and thus decreases the water–oil mobility ratio in water-flooding. The Resistance Factor and Residual Resistance Factor for single phase saturations and slug deployments investigated gave interesting outcomes for each polymer type. PAA gave a more favorable outcome as indications from the flooding suggests the existence of high injectivity associated with the Xanthan flooding. Results from the different Slug scenarios suggest that using smaller slug model would not be favorable for the deployment. Larger slug models yield better RRFs. A combination of larger slugs of CO2 + Polymer gives better results and enables greater injectivity.

The use of dynamic method to determine hydrocarbon in-place volumes in R5000 reservoir in Urad field became necessary when it was discovered that the in-place volumes estimated in previous studies for this reservoir cannot be validated by current production data. A combination of Buckley Leverett and Decline Curve Analysis has been used to significantly reduce the wide range of uncertainty reflected in previous in place volumes estimation. R5000 reservoir in Urad field has two producing wells, one closed-in well and two planned wells; from production data review, it was discovered that estimated in-place volumes was less than optimum. The challenge of re-evaluation to obtain result that both structural interpretation and production performance data will support arose. This paper therefore has deployed Buckley Leverett method with assumption of moderate water drive and gravity drainage to approximate a water flood and as such predict recovery efficiency using the interaction between fluid mobilities, fractional flow and water saturation. Decline curve analysis performed on the producing wells in R5000 reservoir was used to derive technical Ultimate Recovery and a reverse calculation has been done to estimate hydrocarbon initially in-place that supports structure and production data. The recovery factors of three reservoirs in Urad field, namely P1000, H2000 and K3000 have been previously estimated in an earlier study on this field using Buckley Leverett method and result obtained in this work was validated by repeating the BL evaluation for these three reservoirs to reproduce the recovery factor results obtained in earlier study. This work provides a solution to the challenge of estimating hydrocarbon initially in-place volumes in reservoirs where available rock and fluid data as well as fluid contacts information are not reliable.

The evaluation of hydrocarbon in-place volumes in any reservoir depends principally on structural uncertainty, fluid contact as well as rock and fluid properties distribution. This paper focuses on integrated approaches used to estimate hydrocarbon in-place volumes in a reservoir with very limited well spread. This study highlights the importance of carrying out further studies on our brown fields to re-evaluate the earlier estimated hydro-carbon Inplace volumes and its ultimate recovery by integrating historical information (performance data etc) and emerging development plan. About five wells have been drilled in the field and their penetrations are restricted to the eastern flank of the reservoirs. Well test data exist in some of the reservoirs and continuous BHP data were also taken in most of the producing reservoirs. The Mangu field reservoir ‘A’ has over 10 years of production (with limited well spread and few producers). It has produced more than the initially estimated ultimate recovery, over 85% of its initially estimated in-place volumes. Hence the integrated technical team decided to to re-evaluate the in-place using performance-based information and re-evaluation of the expected ultimate recovery (Buckley Leverett Approach). The determination of hydrocarbon in-place volumes in Mangu field was also premised on the re-estimation and prediction of rock and fluid properties variation away from area of well penetrations. In order to determine the average reservoir properties and their associated uncertainties as well as their use for in-place volumes estimation, multidisciplinary approaches have been applied and they include (a) evaluation of well by well logs & rock properties (b) generation of reservoir property maps (c) analysis of pressure and performance data and (d) Static/Dynamic modelling and (e) Determination of Recovery Factors using Buckley Leverett approach and use of Decline Curve Analysis. The result of this study highlights the significance and impact of limited well data spread and its associated uncertainties on reservoir in-place volumes estimation and ultimate recovery determination. An integrated approach has helped to significantly reduce the uncertainties associated with in-place volumes estimation in the Mangu field thereby leading to a commercially viable and timely Field Development Plan.