Applications of data analysis techniques for oil production prediction
Introduction
Petroleum engineers have searched for a simple but reliable way to predict oil production for a long time. Prediction of future production of petroleum wells is important for cost-effective operations of the petroleum industry. Production predictions can assist petroleum engineers in economic forecasts, and the approach often adopted by reservoir engineers is numerical simulation based on log and core analysis results. However, this process can be technically difficult, time consuming and expensive in terms of both labor and computational resources. At the same time, there is a vast amount of production data that is collected and stored, but hardly used. This set of production data can be utilized to build a model to predict production. Simple curve estimation could be adopted for this task but its accuracy is often suspect. Hence, the Artificial Neural Network (ANN) technique has been adopted as an alternative approach. The advantages of neural networks include its computational efficiency, non-linear characteristics, generation properties, and ease of working with high-dimensional data. This paper describes a Decision Support System (DSS) that incorporates both curve fitting and ANN approaches, and presents users with a range of possible solutions in the range of cumulative production and length of production of an infill well. By reviewing the range of possible solutions, the user can determine the best production forecast.
The objective of this study is to present this DSS for petroleum production. The basis of the prediction is the well's initial production and its economic cut-off. The assumption adopted in the investigation is that the production of an infill well is similar to existing wells in the same reservoir, therefore production curves of existing wells can provide useful information for future production of an infill well. However, it is also assumed that an expert user is best able to judge how relevant the information is from the existing wells. Hence, the system provides the best and worst case scenarios based on production curves from the existing wells, and the expert user selects among the plots of predicted production rates from each existing well to decide which model has the best fit. The system also generates a model based on the average production of the existing wells, which is provided to the user as reference.
In addition to generating the best and worst case scenarios of predicted production curves, the system also mathematically models the production curve of each individual well so that production values beyond the historical data can be produced. Both curve estimation and neural network approaches are adopted for mathematical modeling. In the curve estimation approach, the five types of linear, logarithmic, exponential, harmonic and general hyperbolic curve fitting methods were used. In addition, the system can also conduct neural network modeling and generate a model that associates the amount of oil produced per day in a certain month with the month index.
The paper is organized as follows. Section 2 reviews some background literature on traditional methods for predicting petroleum production, and these are primarily curve estimation and neural network approaches. Section 3 describes the methods and tools used to develop the DSS presented in this study. Section 4 describes the DSS. Section 5 provides some experiment results. Section 6 presents some observations and discussion, and Section 7 concludes the paper with summary and future work.
Section snippets
Petroleum production prediction
The five traditional methods of estimating, both physically and economically, remaining reserves of petroleum in an oil well include: (1) by analogy, (2) volumetrics, (3) material balance, (4) decline curve fitting, and (5) reservoir simulation (Thompson and Wright, 1985). Each of these methods can be applied independently and has its strengths and weaknesses. While all five methods can be used for predicting recoverable reserves of a reservoir, the methods have different data requirements. But
Methods and tools
The DSS adopts two basic modeling approaches: (1) estimation curve, and (2) neural network methods. They are presented as follows.
Components of the oil production predictor system
The DSS contains three main components: a user interface, history-matching models, and an analogue predictor. They will be discussed in detail as follows.
Experimental results
The Oil Production Predictor System was used for analyzing two groups of wells. Group 1 contains 14 short-life wells from the Oakly field and group 2 contains five long-life wells from the Midale field in Saskatchewan, Canada. Short-life wells last for less than 90 months while long-life wells last for 200 or more months.
Fitness of models
To investigate performance of the different methods on the training data, a comparison of the predicted values from all the methods versus actual production rate of one well at Midale was made. Among the curve estimation methods, hyperbolic and logarithmic curves display better fitting abilities. However, as can be observed in Fig. 4, the neural network model fits the training data better than the curve fitting methods.
However, as discussed in the previous section, a good fit to training data
Conclusion and future work
In recent years, considerable progress has been made in the development of methods for estimating future production of oil wells. However, these methods are usually either difficult to use or inaccurate. The DSS presented in this paper does not attempt to invent a novel prediction method. Instead, it is based on several simple available methods and gives users numerical and visual illustrations of the results so that experienced users can exercise their judgement and decide which scenario is
Acknowledgements
The authors would like to acknowledge the generous support of a Strategic Grant from the Natural Sciences and Engineering Research Council of Canada. We are grateful also for insightful discussions and support of Michael Monea of Petroleum Technology Research Center and Malcolm Wilson of University of Regina at various stages of the research.
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