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How do you evaluate reservoir performance and uncertainty using decline curve analysis?

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Decline curve analysis (DCA) is a widely used method to estimate reservoir performance and uncertainty based on historical production data. It can help you forecast future production, evaluate reservoir management strategies, and assess economic viability. However, DCA also has some limitations and challenges that you need to be aware of. In this article, you will learn how to apply DCA to different types of reservoirs, how to choose appropriate decline models and parameters, and how to quantify and reduce uncertainty in your estimates.

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Abdelrahman El-Diasty

Vice President, Petroleum Engineering at Prime Rock Energy Capital

1 What is decline curve analysis?

DCA is a graphical technique that plots production rate or cumulative production versus time on a log-log or semi-log scale. The shape of the curve reflects the reservoir characteristics and the fluid flow behavior. By fitting a mathematical model to the curve, you can extrapolate the production trend and calculate important parameters such as initial production rate, decline rate, and ultimate recovery. DCA can be applied to both conventional and unconventional reservoirs, but the choice of the model and the assumptions may vary.

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Vikas Kumar Singh

Digital Oil Field Consultant (DOF) at IPCOS !!Attended ADIPEC 2022 & MEOS 2019 Bahrain!!
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Evaluate reservoir performance and uncertainty using decline curve analysis by fitting production data to decline curves, considering different decline curve models, incorporating statistical methods for uncertainty quantification, and integrating data from well tests and reservoir simulations. The analysis provides insights into reservoir behavior, production trends, and helps assess the range of possible outcomes, contributing to informed decision-making and reservoir management.

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2 How to choose a decline model?

When considering DCA, there are various decline models available, such as the exponential model, which assumes a constant decline rate and is suitable for reservoirs with strong pressure support or low permeability. The hyperbolic model, which assumes a variable decline rate that decreases over time, is applicable to reservoirs with transient or boundary-dominated flow. Additionally, the harmonic model is suitable for reservoirs with high water cut or gas-oil ratio, assuming a variable decline rate that increases over time. Lastly, the stretched exponential model is useful for unconventional reservoirs with complex fracture networks, as it assumes a variable decline rate that follows a power-law function. To select the most appropriate model for your data and reservoir behavior, you can use various criteria such as visual inspection, statistical measures, or physical reasoning to evaluate the fit.

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Abdelrahman El-Diasty

Vice President, Petroleum Engineering at Prime Rock Energy Capital
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There are few points to consider while choosing a Decline Model: - Data Trends: Exponential for constant decline, hyperbolic for variable decline, harmonic for rapid then slowing decline. - Reservoir Type: Hyperbolic for unconventional reservoirs. - Model Fit: Use R-squared and residual analysis. - Practicality: Consider ease of use and industry practices.

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3 How to estimate decline parameters?

Once you have selected a decline model, you must estimate the parameters that define its shape and magnitude. These parameters are usually qi (the initial production rate at the start of the decline period), Di (the initial decline rate at the start of the decline period), b (the decline exponent that controls the curvature of the curve), and n (the stretching factor that controls the slope of the curve). You can estimate these parameters through graphical analysis, curve fitting, or optimization, and should use reliable and consistent data that covers a representative period of the decline phase. Additionally, it is important to assess the sensitivity and uncertainty of your estimates to different data sets and methods.

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4 How to forecast future production?

After estimating the decline parameters, you can use them to forecast future production by extending the decline curve to a certain time horizon or a certain economic limit. You can calculate the future production rate or cumulative production by using the decline model equation and plugging in the values of the parameters. You can also calculate the ultimate recovery by integrating the decline model equation over time. You should be careful when extrapolating the decline curve beyond the range of your data, as the model assumptions may not hold or the reservoir conditions may change.

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5 How to evaluate uncertainty?

When conducting DCA, it is essential to evaluate the uncertainty associated with estimates and forecasts, which can arise from various sources such as data quality, model selection, parameter estimation, and extrapolation. To quantify and reduce uncertainty, you can use error analysis to measure the deviation between the observed and predicted data. Additionally, confidence intervals can provide a range of values that are likely to contain the true value of a parameter or forecast. Monte Carlo simulation can generate multiple scenarios of production by randomly varying the input parameters and evaluate the distribution and risk of outcomes. Finally, sensitivity analysis can examine how sensitive your estimates and forecasts are to changes in the input parameters or model assumptions. Through these techniques, you can assess the uncertainty of your DCA results and communicate it clearly to stakeholders.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How do you evaluate reservoir performance and uncertainty using decline curve analysis?

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1 What is decline curve analysis?

2 How to choose a decline model?

3 How to estimate decline parameters?

4 How to forecast future production?

5 How to evaluate uncertainty?

6 Here’s what else to consider

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