Simulation-Based Technology for Rapid Assessment of Redevelopment Potential in Marginal Gas Fields - Technology Advances and Validation in Garden Plains Field, Western Canada Sedimentary Basin
- Yueming Cheng (West Virginia University) | Duane A. McVay (Texas A&M University) | Jianwei Wang (Texas A&M University) | Walter B. Ayers (Texas A&M University)
- Document ID
- Society of Petroleum Engineers
- SPE Reservoir Evaluation & Engineering
- Publication Date
- June 2008
- Document Type
- Journal Paper
- 521 - 534
- 2008. Society of Petroleum Engineers
- 4.1.2 Separation and Treating, 5.5 Reservoir Simulation, 3.2.3 Hydraulic Fracturing Design, Implementation and Optimisation, 1.6 Drilling Operations, 4.1.5 Processing Equipment, 5.6.9 Production Forecasting, 3.3.1 Production Logging, 5.1 Reservoir Characterisation, 5.1.1 Exploration, Development, Structural Geology, 4.6 Natural Gas, 5.7 Reserves Evaluation, 5.1.5 Geologic Modeling, 5.6.4 Drillstem/Well Testing, 5.5.1 Simulator Development, 5.2.1 Phase Behavior and PVT Measurements, 5.8.1 Tight Gas, 4.3.4 Scale, 1.7.5 Well Control, 5.5.8 History Matching
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It is often difficult to quantify the redevelopment potential of marginal oil and gas fields because of a wide range of depositional environments, variability in reservoir properties, a large number of wells, and limited reservoir information. Evaluation of infill potential in these fields with traditional simulation methods is time-consuming, labor-intensive, and frequently cost-prohibitive. Without adequate assessment technology, some unprofitable infill campaigns may be initiated, while other promising infill campaigns may be terminated prematurely because of disappointing early results.
In this paper, we present a simulation-based regression technique to assess infill-drilling potential in marginal gas fields. With limited, basic reservoir information, this technique first estimates the spatial distribution of subsurface reservoir properties by rapid history matching of well production data. We implemented a sequential regression algorithm to estimate, from available flow-rate measurements, not only the permeability distribution, but also the pore-volume distribution. Future production is forecasted and infill- drilling potential is determined using the estimated permeability and pore-volume distributions. Because the method uses an approximate reservoir description, it identifies regions of the field with promising infill potential rather than individual-infill-well locations.
The proposed technique provides rapid, reliable, and cost-effective assessment of redevelopment potential in marginal gas fields. In this paper, we first validate our approach using synthetic reservoir data. We then apply the approach to the Second White Specks formation, Garden Plains field, western Canada sedimentary basin. The prediction of infill potential in this gas field, which has more than 700 wells, demonstrates the power and utility of the proposed technique.
Infill drilling can be an effective process that can accelerate field development, increase field oil-gas-production rates, and add reserves. Infill drilling, particularly in tight, marginal gas fields, represents a significant opportunity to increase production rates and reserves. The more than 200,000 low-rate, or "stripper," gas wells in the US lower 48 states produce approximately 10% of onshore gas production (Exploration and Production Technologies). Before implementing any program to increase well density, the potential of infill drilling must be assessed reliably to justify such infill activities both technically and economically, especially for marginal fields. Without a reliable evaluation of infill potential, some unprofitable infill campaigns may be initiated, while other promising infill campaigns may be terminated prematurely because of disappointing early results. The quantification of infill drilling for marginal fields represents a significant technical challenge, not only because of the complexity (the result of a wide range of depositional environments and large variability in reservoir properties) involved in the evaluation, but also because the evaluation often has to deal with a large number of wells (hundreds to thousands of wells is not uncommon), limited reservoir information, and time and budget constraints.
It is well recognized that the performance of infill wells depends on many factors and their interaction. These factors include reservoir heterogeneity or spatial variations of reservoir properties (such as permeability, net pay, porosity, and gas saturation); well interference; and available reservoir energy (pressure). The combination, or interaction, of these factors determines how much oil-gas can be produced in the future. These factors and their interaction must be well understood and adequately evaluated to provide a reliable assessment of infill-drilling potential. To achieve this, assessment technology must be able to address the previously mentioned engineering concerns and, at the same time, must be cost-effective and able to handle the practical issues of having a large number of wells and limited reservoir information.
In general, an integrated reservoir study, which typically requires detailed geological, geophysical, petrophysical, reservoir-engineering, and reservoir-simulation studies, is the best way to evaluate infill-drilling potential. Integrated reservoir studies can adequately address the engineering concerns discussed previously. However, for marginal fields, high costs and the lack of relevant reservoir data make such detailed studies difficult to perform and to justify economically. One of the key tasks in a reservoir-simulation study is to calibrate the reservoir model by matching the historical production data. In a traditional simulation study, however, it is very challenging and time-consuming to achieve a history match in a field with hundreds or thousands of wells.
In the past decade, some authors have proposed statistical moving-window techniques to provide a rapid and cost-effective assessment of infill-drilling potential in large, tight gas fields (McCain et al. 1993; Voneiff and Cipolla 1996; Hudson et al. 2000). These techniques perform a statistical analysis of production data using defined performance indicators, such as the best 12 consecutive months of production (best year) and the decline ratio for the best year. The performance indicators serve as proxies for reservoir properties, production response, and reservoir pressure. On the basis of the comparison of indicators between old and new wells in areal "windows?? throughout the field, judgments are made regarding the interference between the existing wells and whether pressure depletion is occurring. These judgments are then used to estimate the potential for infill drilling in each window. While this technology can be a useful screening tool to determine which regions of the field have potential for infill drilling, the prediction errors for infill-well performance can be quite significant; additionally, interference effects become complicated and reservoir heterogeneity increases (Guan et al. 2002).
To improve upon moving-window methods, Gao and McVay (2004) proposed a simulation-based regression approach for the rapid assessment of infill potential in gas-well fields. Using readily available data (such as well locations, production data, and fieldwide average values of porosity, permeability, saturation, and net pay), their approach provides an estimate of the heterogeneous permeability field by inverse modeling. Using the reservoir model and the inverted permeability field, the performance of infill wells is predicted. The authors show that this simulation-based regression approach results in more accurate predictions of infill performance than do the moving-window statistical methods (Guan et al. 2002). However, while their reservoir model accounts for permeability heterogeneity, all other reservoir properties remain fixed at their initial values. Thus, their model does not condition the hydrocarbon-pore-volume distribution (resulting from the porosity, net thickness, and hydrocarbon-saturation distributions) to the production data. In marginal reservoirs, the initial geological model is often based on incomplete data and analyses and, in some cases, may consist simply of the constant average values of each reservoir property. Thus, the initial geological model may possess significant errors. Consequently, while infill performance may be predicted more accurately with the simulation-based regression approach rather than with the moving-window methods, because the reservoir model is not able to adjust the pore-volume distribution, predictions for infill wells and existing wells may still contain large errors (Gao and McVay 2004).
In this paper, we advance the simulation-based regression approach to the rapid assessment of infill potential by presenting a new inverse-modeling algorithm. This algorithm implements a sequential inversion of both permeability and pore-volume distributions through an integration of production data. Thus, starting with the estimated average reservoir properties (net thickness, porosity, gas saturation, and permeability) or a prior geological model, we can now obtain not only an estimate of the large-scale permeability distribution, but also an estimate of the pore-volume distribution. In the following sections, we first present the development of the sequential inversion technique to estimate the distributions of permeability and pore volume by conditioning them to historical production data. We then validate this new technique with synthetic and field examples and, finally, demonstrate its application to assess infill potential in the field example.
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