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.
Integrated Asset Pressure Balanced Modelling involves the coupling of the reservoir dynamic and surface facilities models into a single integrated tool that allows the simulation of the whole oil/gas field system. The pilot study was conducted on the company’s export gas portfolio using pressure balanced model (PBM) in a Hydrocarbon Field Planning Tool (HFPT) platform. In the simulation, HFPT uses a pressure-balanced solution of the integrated system: from the reservoir(s), through wells and surface facilities, to the delivery point. A wide range of fluid models is available, from simple gas-condensate and black oil PVT models to multi-component models with EOS flash calculations. HFPT provides optimization functionality for maximizing the returns in oil and gas fields, while accommodating operational preferences for production allocation and network constraints. The results from this pilot indicate a successful integration of all subsurface discipline and surface data to create a pressure balance model that support our export gas supplies to NLNG. It also provides the platform to test the impact of the supply/facility constraints on our different portfolio projects.
Hydrocarbon reservoirs require steady evaluation in terms of performance prediction and resource volume estimation. These evaluations are planned and carried out from point of discovery till maturation and abandonment. Full field reservoir simulation model is the conventional tool for reservoir performance analysis via planned or existing wells. Material balance models were developed to carry out simple reservoir analysis on tank basis for resource volume estimation and prediction. Well predictive material balance model has proved to be a quick and handy tool capable of doing reservoir analysis both at reservoir and well levels. In this paper, we will be discussing the methodologies, basic workflows and technical principles of building a simple well predictive material balance model. This tool is not a replacement of more robust reservoir simulation model but a quick win in terms of time and cost. The study shows the implementation of this tool for a resource volume estimation and well performance prediction. It covered evaluation of saturated reservoirs with concurrent oil and gas production. The results were compared with reservoir simulation and conventional decline curve analysis results for robustness check. Important data such as relative permeability, well models (IPR and vertical lift curves), current fluid contact and PVT data are some of the essential data required to build a good predictive material balance model. Keys steps taken in building this model are: Data gathering and validations Model Construction History Matching/ Simulation Prediction (reservoir and well) Result analys The Results show good comparison with other methodologies (Simulation and DCA). The wells predictions were good and compare closely with actual well production.
Portfolio optimisation is the process of choosing the proportions of various assets to be held in a portfolio, in such a way as to make the best use of the opportunities in that portfolio in any prevailing circumstance. This is a component of full-wide portfolio management which is an ill-defined and often over-used term. Whether or not a company's portfolio solution entails consolidating data into a spreadsheet or employing modern portfolio theory to develop an efficient frontier, the goal is to ensure long-term business success by optimising value returns at the minimum risk. Since June 2014, international oil and gas prices have been declining and currently (Dec 2015) below 40 USD per barrel and 2.5 USD per million Btu respectively (Fig 1). This has brought much pressure on funding availability both on the sides of national and international oil companies. To fully manage the SPDC's gas portfolio, innovative and sound processes were initiated to optimise and screen a very significant part of our portfolio to ensure that the company still meets and sustains her gas supply obligations with optimal value returns. Prioritisation and ranking of different projects using known parameters and also generating portfolio sensitives using PetroVR tool provided us the foundation for robust decisions on the way forward. Problem Statement: Evaluate different portfolio options to meet supply agreements and obligations.Prioritise all projects in the portfolio and rank them considering technical, commercial and other non-technical indicators. The methodologies applied in resolving the above problems were along two work streams namely: Build an integrated gas portfolio model using the PetroVR tool. This was built consistent with latestcompany's operating plan and creating multiple scenarios. The model has production forecasting and economics evaluation/screening capabilities. Therefore, it was possible to check each portfolio scenario in terms of ability to meet gas supply and their economics value.Project prioritisation tool. This is an in-house tool with analytical abilities. Key input data are ultimate recovery, gas Production rate, CAPEX, UDC, NPV, strategic considerations, etc. The results of these evaluations have helped the company make appropriate decisions on its gas supply strategy and ensuring projects with capacity to meet obligations are matured in priority order.
Gas portfolio development and the need to support Nigeria's Gas master plan is currently a high priority effort in government and international companies. Therefore, it is important to have a standardized approach for the development of the Gas Cap reservoirs. This guideline will provide a clear process to support the asset/study team in securing appropriate approvals from the government regulatory body (e.g. DPR) for the development of the gas cap resources. Gas categorization guideline is adopted as a classification system for the company's gas reservoirs to help demonstrate the timing of availability of gas cap production. The classification is driven by the time remaining to produce the economic ultimate recovery from the oil rims associated with the gas caps and processing plants/evacuation ullage availability. The objectives of the guideline are: To facilitate development planning and gas forecasting via a transparent picture on what the gas resources categories are. While existing fields mature, the development decisions regarding the oil rims become focused on ever reducing infill drilling targets. It is important to be aware how these decisions impact the availability of the gas cap production. The classification helps to provide clarity and transparency in this respect. This clarity aids to demonstrate the robustness of the Company's gas delivery promise.To help in defining standardized approach in getting approval for gas cap development from Department of Petroleum Resources (DPR). In defining this approach, a concise Gas cap release methodology is developed to guide asset and study teams in taking decisions on Gas cap development. A clear workflow was developed for Gas Cap Development and subjected to company's internal assurance process. Proper value assessment is done comparing the oil development and the company's gas requirement. Several sensitivities were carried out on the reservoir gas cap blowdown timing and the total reservoir NPV against the different GCBD timing (Figure1). Figure 1GCBD Optimal Timing Sensitivity
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