An Integrated Production Modelling process is presented that is used to automate CSG (Coal Seam Gas) production forecasts from reservoir to sales. This involved incorporating type curves representing future reservoir performance into a hydraulic model of the surface network. One of the key challenges was to convert time-dependent decline curve forecasts into time and pressure-dependent reservoir-well models suitable for Integrated Production Modelling process. Results are compared to other methods with reduced simulation time, improved accuracy and scenario analysis. This innovative approach used a combination of techniques to incorporate type curve based reservoir models into two large integrated production models. The first model consisted of 250 wells and a single compressor station and the second model consisted of 500 wells and multiple compressor stations. Different techniques were applied for wells that were pre or post dewatering and incorporated into a surface network modelling tool. This allowed production forecasts to be generated automatically for the single compressor station model and semi-automated for the multiple compressor station model whereby the system was solved to meet demand or to maximise production taking into account constraints. Type curve data was converted to a ‘tight gas’ reservoir model which showed a good match for wells post dewatering but not for wells that were still inclining. A combination of proprietary scripting in a network simulation program and macro code in Excel were used to handle system constraints optimisation. The time taken to run each scenario reduced significantly as the restriction moved from a human to the amount of computing power. Accuracy and repeatability of results also improved due to models being setup and solved in a consistent manner, thereby removing discrepancies associated with manually driven models. This allowed for more scenarios to be modelled in the time allocated to the production forecasting processes allowing for improved analysis and decision making. Previously type curved based reservoir models could not be solved by a surface network modelling tool automatically without human intervention. This was not possible due to the size and complexity of the surface network and because of the lack of time and pressure-dependent reservoir-well models suitable for Integrated Production Modelling process. The process outlined in this paper overcame this challenge.

Quick evaluation of reservoir performance is one of the main concern in decision making. Time consuming data preparation and processing, and data uncertainty (geological, petrophysical, reservoir engineering) limit the use of numerical simulators in addition to long term response for reservoir management. Effective reservoir management needs quick action regarding injected fluid distribution to improve areal and vertical sweep efficiency during secondary and tertiary recovery processes. Therefore, simpler methods that provide quick results to complement or substitute reservoir simulation are important for reservoir monitoring and management. Capacitance Resistance Model (CRM) is one proven method to address the above challenges. The CRM model is based on the hypothesis that reservoir performance can be inferred from analyzing production and injection data and a simplified analytic model structure. CRM is an input-output and material balance based model that use continuity equation to quantify the connectivity among injector and producer wells, time constant and productivity indices. In this study, a CRM model was generated to build a forecasting waterflood model by to matching historic production and injection data. After history matching, the model can be used to: (1) effectively forecast oil and water production, (2) evaluate reservoir and production-injection data uncertainties, and (3) optimize injection settings to improve oil recovery factor. CRM methodology is used as a cooperative approach to reservoir simulator generated streamlines. Different case studies were designed to investigate the robustness of CRM by providing different levels of complexities as compared with numeric simulation results. In the first case, a constant flowing bottomhole pressure and injection rate was considered, and in the second case, changes to flowing bottomhole pressure, injection rate and productivity index per each time step were considered. The CRM was able to replicate the synthetic historic data generated using numerical simulator within a satisfactory error, i.e. less than 7% in the worst case. The CRM injector producer allocation factors were also in line to those computed using streamlines. In both cases, the CRM model showed a higher allocation factor for the injector producer pairs which had better connectivity allowing the evaluation of waterflood performance by pattern while identifying the poor sweep efficiency areas. In a later optimization exercise, a dry oil production objective function was maximized subject to the constraints in the injection system. Thus, strategies derived from CRM model increased production in the range of 12% with associated water cut reduction just by reallocating injection rates.

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It is widely recognized by oil industry that reservoir characteristics such as natural heterogeneity, spatial variability of permeability, net pay, porosity and spatial distribution of oil and water saturations control the fluid flow in porous media, reservoir performance, development strategies and the economics returns of investments of field development plan implementations. Nonetheless, most of the time few data are available for projects and fast evaluation of reservoir performance is needed for decision making. Understand the impact of reservoir parameters variability over pressures and saturations history match require multiple realizations of numerical simulations. Managers tend to avoid costs and production losses related to data acquisition. Pressure and rate transient analyses (PTA&RTA), along extended production time, allows the reservoir characterization (drive mechanisms, boundaries conditions) and understand near wellbore characteristics, however, PTA&RTA alone does not provide pressure and fluid distribution prediction beyond the wellbore, it requires the integration with reservoir simulation. This paper provides an integrated reservoir characterization workflow which allows reservoir engineers to identify production/reservoir uncertainties and constraints to evaluate different field development strategies to maximize the ultimate oil recovery in a short period of time. Pressure Transient Analysis (PTA) address near wellbore effects and help to identify gas cap or aquifer strength, reservoir boundaries as well as recognizing sealing/non-sealing faults. For this project, static pressure was not available to perform history matching, but real time Pump Intake Pressure (PIP) was measured from Electric Submersible Pump sensors. Vertical lift performance curve was determined by using well completion data at the mid-point of perforation extrapolating PIP to bottomhole pressure (Pwf). Rate Transient Analysis (RTA) utilizes continuous production and flowing pressure data to characterize the reservoir and completion and it was key to understand reservoir performance, and provide insights for rapid and reliable history matching. This workflow was successfully applied in South America reservoir. To accomplish goals, it was necessary to perform a quick reservoir characterization and analysis - reservoir compartmentalization, PVT property distributions, rock properties distribution, and well performance history – to understand reservoir pressure and production behavior. Unstructured refined grid principles (Voronoi Grid) were applied to build a relatively small and simple model that considers all the required physics of the problem. The estimated reservoir properties from petrophysical analysis were validated against PTA&RTA thus honoring the near wellbore effects. The resulting model permitted the generation of key field development strategies considering additional well placement and completion technologies and best production operational practices, as well as the characterization of major uncertainties related with the reservoir-well system. Once model was initialized under a satisfactory level of tolerance (difference between the original oil in place determined by numerical reservoir model and the static model less than 2%) the history matching process began. Henceforth, a history matching workflow was generated to optimize the number of sensitivities and prioritize variable of greater significance. The history matching process improved pressure and production match and reduced CPU time approximately by 80% and 60% respectively. Aquifer strength for each formation was determined as a result of these analyses. The reservoir and production uncertainties allowed generation of a new surveillance plan to improve reservoir characterization and performance in the short term. Finally, a base case scenario was generated as a starting point to determine the recovery factor and identify new opportunities to develop oil and gas recoverable reserves of the field. An optimum scenario was generated to reduce water production and increase oil recovery for each formation resulting in 17.6 % reduction of produced water, with a simultaneous 13 % increase in overall oil recovery and 8 % project profitability. Additionally a series of recommendations were also derived regarding to: 1. Guidelines to generate a more reliable, consistent, and economically profitable field development plan, 2. Data acquisition plan to minimize impact of uncertainties in the field development plan, and 3. Visualization of improved oil recovery opportunities by carrying out gas, water, and EOR processes like polymer injection.