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Production forecasts provide fundamental input to upstream business decisions, for example in resource volume reporting, field development and production planning. Very often in mature fields, having sufficient cumulative production, field forecasts are performed based on aggregating type curves for new wells and decline curve analysis for existing wells. Conventionally, they are derived by manual trend fitting which is subjective and any iteration incorporating new data is time consuming. The advanced analytics approach presented in this paper can provide rapid and credible forecasts along with more robust quantification of uncertainties as compared to the conventional manual approach. The first step involved in the forecast automation using advanced analytics is data integration in which the production, geological, and surveillance data are combined. Then integrated dashboards are created to infer trends in production behavior. Based on these trends at the most granular forecast level, petroleum engineering based algorithms are developed to automatically select the decline period. Thereafter, multiple scenarios of historical data are generated based on historical allocation and measurement uncertainty. Through each set of these historical scenarios, best fits curves following Arp's equation are extrapolated into the future to generate multiple forecast scenarios. Finally, field level forecast is generated by probabilistically aggregating individual granular level forecasts. The automated forecast program developed is computationally very fast as 500 forecast scenarios at the well level can be generated in 30 seconds, while the full field forecast with 50 wells takes about 30 minutes to generate. Automation of decline period selection and curve fitting eliminates the subjectivity by standardizing the process and reduces the chances of manual errors. Abandonment criteria, discounting and uptime variations can easily be accommodated in the automated process. Visualizations are utilized at each step for quality check and analysis. Forecasts from alternate methodologies are used to validate the forecast ranges coming out from this method. Uncertainties quantification in this approach is found to be more quantifiable and consistent compared to the conventional deterministic approach. Production dashboards created in this workflow by integrating production, surveillance, geological data and forecasts are a very effective tool to perform field reviews and communicate outcomes. The approach described above for decline curve analysis can easily be extended to any type-curve based forecasting. Automation of performance-based decline curve and type curve forecast methodologies has the potential to reduce huge manhours involved in their periodic updates by an order of magnitude that can be utilized to carry out other critical analysis. It is very useful in mature assets with large well inventory, huge dataset and where continuously new data are being added. Standardization of workflow, implementation ease and accuracy will tempt practitioners to use it and thereby develop skills in data analytics. |
