Production forecasting in shale reservoirs is a challenging task because of the complex influences of geology, lithology, stimulation practices, etc. The large well count makes history matching and forward simulation particularly time consuming and laborious. In such a context, it is important to consider alternative methods, and to this end, we have developed two new methods of forecasting production. The first method uses data mining techniques, which allow the analysis of large quantities of data to discover meaningful pattern and relationships. These can subsequently be used for prediction. Some common data mining tools are neural networks (NN), genetic algorithms (GA), and self-organizing maps (SOM). Our method uses NN for predicting the future performance of a shale gas well based on historical production data of the previous year. The decline in production is captured during the NN training process and applied to the production data during the forecasting phase. The model is simple, elegant and fast and is able to forecast production in an unconventional play with reasonable tolerance. The second method uses time series analysis. It the trend, changes in value, rate of decline, and correlation with the past to generate a rapid and accurate forecast. The stock markets use this technique, and it is safe to say that if it can predict the stock ticks, then it can yield good results on a fluctuating, but surely declining, production rate. These methods are elegant and fast and are able to forecast production in an unconventional play with reasonable tolerance. They are not data intensive and can also be automated to be applied to a large number of wells, which makes them particularly useful in integrated operations in which a comparison of actual versus predicted behavior would enable operators to quickly identify problem wells for a more detailed investigation. The methods were applied to wells from the Barnett, Bakken, and Eagle Ford plays.

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The McCully Field (New Brunswick, Canada) is highly instrumented and generates a massive quantity of high-frequency data stored in a data historian. Data time range and frequency of interest had to be manually retrieved through spreadsheet macros for analysis, plotting, and gas allocation. As the production history grew, the amount of data generated was overwhelming and unconsolidated, making the task of manual data handling and visualization both difficult and time consuming. The implementation of a production data management system resulted in a fully automated, end-to-end workflow that acquires five-minute data from the Supervisory Control and Data Acquisition (SCADA) system into the operating database; performs accurate allocation of gas, condensate and water volumes; as well as creates and distributes daily production summaries to a custom email list in a matter of minutes. The implementation of an automated approach was driven by five main objectives: automate production data acquisitionoptimize field production through real-time well surveillanceincrease data processing speed (reduce time spent on data handling, preparation, cleansing and reporting)take advantage of existing high-frequency databaseimprove communications regarding well performance between office and field operations After a successful implementation, data acquisition time has been reduced from a manual 30 minutes to an automated 5 minutes each day. Daily production reports, instead of only being accessible through the server, are now automatically emailed to a distribution list within the company. Real-time well surveillance is now possible from the main office and not only through the SCADA system in the field, which provides the engineering group with a better understanding of individual well performance and also allows any production disruptions to be identified early and resolved efficiently. Finally, the consolidated database is now integrated with engineering analysis tools for further use of information, which ultimately increases the effectiveness of the technical team.

This paper presents the advances in Production System Optimization for the largest gas field in Mexico. The Burgos Asset is a large gas brown field with reservoir characteristics like gas-loading backpressure, reduced permeability and tight gas formations where production declines rapidly. Due to the large number of wells (more than 3500 active wells) and the fact that 95% of the measured parameters are obtained by field operators, it is difficult to continuously monitor and to plan the usage of operational resources.To help solve this situation, PEMEX and the service company (Schlumberger) implemented a production surveillance system that gathers all operational information providing storage, quality control, and developed engineering processes to estimate gas rates and liquid loading, monitors KPI and detects anomalies in operational events.Implementation of this operational surveillance environment started in 2007, with a functionality that has been focused on monitoring KPI calculation and event analysis at well level. Due to the large number of wells and activities carried out by the Asset, the need arose to generate additional workflows that contribute to the production optimization process, candidate selection workflow for workover and artificial lift installation, allowing upscaling of the current solution to a level that supports the technical and economical decisions of the Asset.As part of this requirement the team took the initiative to incorporate workflows of candidate recognition for workover, debottlenecking and optimization of a particular set of facilities, allowing the Asset to take corrective action in these areas and to plan the recommendations accordingly. Additionally, as proof of concept, an intelligent candidate selection system was implemented for artificial lift installation opportunities, using artificial intelligence tools such as data mining, improving decisions and results.The initiatives mentioned above help to increase, validate and rank the Asset's diverse candidate basket and to integrate the economic constraints into the decision making process. The positive results that have been obtained in these focalized areas show a significant opportunity to be up scaled for the whole Asset.