Developing a procedure to sort and extract useful information from a mass production data
MetadataShow full item record
Abstract The Rubiales field is a multi-well cluster field which creates severe amounts of production data. Rapid expansion has lead to massive increase in oil production. A consequence is missed opportunities of optimization in data management. The aim of this master thesis was to examine methods to extract and organize information from a multi-well mass production data set. This is for utilization of the data in modeling, analysis and decision making in operation of the field. Data from three years of production was used to create the procedure. A literary review was made to introduce the concepts of data management. The data management system will discuss databases, processing and analysis of data together with important components of data management. The literary review also presents the benefits of using data management in the oil industry and presents the concept of asset management. A case study was used to demonstrate the need and merit of production optimization using data management. For a small amount of data it was shown that data management could be beneficial. Wells proved underperforming according to measures set in the “Reserves Certification Report” by RPS Energy (2). An automated model was built to enhance the use of data management and the processing was performed on historical data. Three years of data was given as daily data in separate Microsoft Excel files containing large amounts of information. The production data was extracted and transferred from the individual Excel files to a database in Microsoft Access. Here the files are sorted in daily Access objects containing only the production data. Two different routines were then written to extract and process the data. The processing was an automated process that created graphical information and sorted lists for improved decision making. The master thesis demonstrates a procedure that sort and extract useful information from a mass production data set. It presents a procedure of transforming data to information in a very short time. The model is very limited, but graphical information enhances decision making while filtered and sorted lists reveal underperforming wells. There are enormous potential in data management and the thesis can be seen as an incentive to expand the use of data management. Even though the model is made by a production technology student with very limited experience in data management and programming it is easy to use. Combining the skills of a production engineer with experience from the field with a programmer, a data management model would probably improve the value of the field. The principal of the model could be used on real-time data and simulated data which would lead to shorter response time on closing underperforming wells. An economical extension could also be beneficial. Closing wells and other field operating decisions should be based on economics, not static constraints based solely on oil production and water cut.