A multi-tank model is presented that was used to evaluate the volume of gas produced from an undeveloped gas reservoir as a result of sand-to-sand juxtaposition with a developed oil rim reservoir. An innovative approach of using Microsoft Excel via OpenServer to link MBAL model to history match reservoir pressures in a multi-tank model, while considering all the reservoir uncertainties was adopted. The process helps to save time in Reservoir Management. An oil rim reservoir with future gas development seemed to be communicating with an undeveloped gas reservoir via sand to sand juxtaposition based on the pressure data taken during the drilling of one of the wells and fault seal analysis. This clearly showed depletion in the undeveloped gas reservoir. Through a multidisciplinary approach, the two reservoirs were built into tank models and connected using a transmissibility model. The resultant model was history matched using an experimental design approach and contacts calibrated prior to running simulation and prediction. The result showed the quantity of Gas Initially in Place (GIIP) in the undeveloped reservoir that has flowed into the developed reservoir and has possibly been produced already. This insight provides a quick analytical understanding on the resource volume impact of this phenomenon on both reservoirs with respect to their future gas development. This has led to the need for a revised development plan for both reservoirs with respect to future gas production. The novelty of the use of experimental design with MBAL multi-tank model in this scenario is in the ability to history match the model in reasonable time. This is achieved while effectively managing reservoir uncertainties. This is critical for key business decisions on reserves booking, business planning, general reservoir management and production.

A quick evaluation of reserves for new opportunities (e.g. perforation extension and other work over types) in reservoirs with distinct geological units and features is possible using a multi tank MBAL option. This saves time while still having results closely matching more detailed simulation models besides reservoir management due to subsurface uncertainties. In cases where a reservoir is naturally separated into units with the aquifer as the only common communication base or where there are constricting saddles which in production time allows preferential sweeping of the reservoir posits the possibility of separate tanks. Multi-tank MBAL has been used in this scenario to generate a production forecast for a work over opportunity in Reservoirs A, B & C. This methodology transmits the segregated accumulations of the reservoirs into tank sectors and connects them using transmissibility value to a common aquifer leg in a multidisciplinary approach. Resultant model is history matched and contacts calibrated prior to prediction especially when present contact information exists. The methodology as opposed to a single tank MBAL model gives better calibration of contact movement and forecast of the future and existing opportunities, thus giving credence to more robust reservoir management plan and resource volume estimation for the work over project. The MBAL multi tank methodology is a handy improvement tool for brownfield production forecast within the Wells, Reservoir & Facility Management domain especially where no 3D dynamic models exist.

The evaluation of the recoverable hydrocarbon volume and further development opportunities in complex reservoirs (where two or more reservoirs are hydraulically connected) primary challenges the engineer faces in managing such reservoirs. In this study, multi-tank material balance models have been built to solve these problems. The key criteria for a robust material balance modelling of hydraulically connected reservoirs in a single system are: (i) transmissibility across the reservoirs should be properly defined. Transmissibility is a major modelling component in achieving sound multi-tank MBAL models. It is useful to the estimation of the rate of aquifer movement across the reservoirs. (ii) Good understanding of the geology and production data of the reservoirs is helpful in estimating the appropriate transmissibility. (iii) Sufficient and quality Carbon-Oxygen logs, BHP and production data. CO logs are very important for proper calibration of hydrocarbon contact. Accurate BHP data is critical in the establishment of dynamic communication and matching of simulated versus measured reservoir pressure. In this paper two cases with over 30 years of production history are discussed in detail including the full methodology and the associated results. The results from these studies show good and reliable outcome which has provided the basis for the reported hydrocarbon resource volumes of the reservoirs (B1.0X, B1.0N, C1.0X, E9.0X, E10.0X, E11.0X, G1.0X, H1.0X) Results were compared with other methodologies (existing simulation models and DCA of NFA wells) and indicate good comparisons. The number of development opportunities in the 8 reservoirs were optimised from 22 to 20 wells using the multi-tank material balance model. Despite some known limitations of material balance generally, multi tank material balance model has proven to be a simple and reliable methodology in evaluating complex reservoir system with hydraulic communication. Especially, in situations where time and budget constraints will not support full field reservoir simulation modelling.

Nigeria's crude oil reserves and production capacity growth have stagnated over the past decade leading to the apprehension of whether the country is running out of oil or running into it. Existing methods of predicting production capacity and reserves growth to address this apprehension have series of gaps which this research addressed namely inconsistent and optimistic peak oil predictions from several authors, over curve-fitting, lack of details on investment underpinning the forecast, non-relationship with international crude oil price dynamics, omission of structures of the system being modeled and non-flexibility for scenario modeling. This research offers a long term perspective to modelling and forecasting using system dynamics approach with reserves replacement ratio (RRR) as proxy for status of oil and gas industry in Nigeria. The research successfully formulated mathematical models, built Nigeria Upstream Petroleum Integrated portfolio system dynamic model (NUPIM), history matched the reference modes from 1958 to 2015 with quality of the match assessed by visualization and statistical method of multiple regressions. Then, the research forecasted Nigeria oil production capacity and reserves from 2018 to 2035 and performed economic evaluation under various growth policy scenarios to provide a long term perspective of the industry. International crude oil price dynamics was endogenously modeled, history matched and predicted in order to capture its dynamic impact on reserves and production capacity growth. Under the present state called the passive growth policy scenario where a modicum of investments are being made in exploration and development, RRR is estimated at 60% as at 2016,which is evidence of the running out of oil phenomenon. A radical investment policy of injecting some 10 billion USD annually for five years into exploration and development produces RRR > 100%, but this is short-lived. A more distributed fund injection of some 5 billion USD over 10 year period in the optimal investment policy scenario, stimulates growth and guarantees an RRR that is greater than 100% over a longer period of 8 years thereby reversing the crude oil running-out trend. The incremental economics under the present growth policy (passive state) returned negative NPV @12.5% of ca 3 billion USD suggesting the incremental investments in exploration/development to generate new reserves and capacity in the oil industry under the current passive growth policy are value destructive. Having closed identified gaps in knowledge from this research, it is recommended that government and investors should use the system dynamic approach, appropriate fund injection and exploration/development investment mix in addition to governance restructuring to reverse the apparent running-out-of-oil trend in order to drive desired growth in reserves and production capacity and to truly make the oil and gas industry the enabler of economic diversification in Nigeria in the long run.

Petroleum Fiscal Systems refer to the arrangements between a host government and an oil and gas exploration and production company to explore, develop and produce hydrocarbons. Royalty term is a key element of any petroleum fiscal policy and it could be fixed-by-terrain or sliding by volume and petroleum price. Nigeria like most countries operates a fixed-by-terrain royalty system. The defects of such a system include negligence of the size of the firm and the price of hydrocarbon in determining royalty terms. This is attended by negative shock on profitability of small firms and their operations on the one hand and the take statistics of the host government on the other. For example marginal field operators find it difficult to operate under fixed-by-terrain royalty system. All previous investigators on fiscal terms have focused quantitative evaluation on impact of fiscal terms on profitability indicators vis-à -vis impact on government and contractor take statistics without extending to proved reserves impact. This paper fills this gap. This paper focuses investigation on quantitative analysis of impact of variable and fixed-by-terrain royalty terms on proved reserves using an archetypal onshore producing oil and gas asset, existing royalty term in Nigeria as well as the archetypal sliding royalty terms in Nigerian 2012 draft Petroleum Industry Bill. A proved reserves economic spreadsheet model based on SPE Petroleum Resource Management System (PRMS), sliding and fixed royalty systems was developed and used for the study. The investigation shows that with two-tier sliding royalty system (based on volume and oil or gas price) impact on proved reserves could be positive, in effect meaning less exposure on proved reserves in existing assets than under fixed royalty term. Also under a period of global low oil price like in late 2014 to early 2016 where oil price has dropped by close to 250%, the firm may have positive effect on proved reserves due to reduced royalty payment used in economic limit test analysis. These conclusions should guide policy makers and negotiators in deciding royalty terms in fiscal policy formulation.