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Data-Driven Oil Fields 2017

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35 EPMAG.COM | DATA-DRIVEN OIL FIELDS | JANUARY 2017 shows an example of the emissions summary, depicting the geographic basins and their emissions in CO 2 e. The prepara- tion for GHG reporting consisted of a three-stage preparation process to build on each other in order to validate the results. Stage 1: Data Collection The producer enlisted a centralized data warehouse and business logic software as the initial part of its solution to automate the collection, integration and reporting, all with an emphasis on quality assurance and usefulness to manage- ment. Figure 1 shows an example of the emissions summary, depicting the geographic basins and their emissions in CO 2 e. The preparation for GHG reporting consisted of a three-stage preparation process to build on each other in order to validate the results. Stage 2: Integration During this stage, the data fields were further assessed to de- termine the spheres needing to be integrated. They can be ei- ther indirect data or direct emission calculation inputs. Once that is accomplished, a common identifier is established to allow the integration of data sources from disparate systems. Hierarchical and non-hierarchical equipment were paired so they could be mapped by well pad. An example was the rela- tionship coupling of engine-driven compressors and its en- gine. In this way the data can be fully integrated by individual well, segregated by parameters such as equipment type, activ- ity or annual production. Stage 3: QA/QC To ensure that the GHG rule was implemented to its fullest extent with the necessary stated quality, the software had to both integrate the previous stages but also have the necessary business log- ics embedded to perform data analyses and valida- tion. The logic was em- ployed to check individu- al systems for information that was either missing, inaccurate or duplicated. It was also used to cross- check between systems. The software further ver- ified the list of wells and substantiated the equip- ment inventory and count at each well site. Results At the conclusion of the three-stage process, the producer's environmental department personnel were able to generate a single clean, validated dataset for GHG emissions calcu- lations going forward. Once the exercise was initially com- pleted, the producer estimated that they would save approxi- mately 80% of the future labor costs. In their previous efforts, the task required more than six man-weeks to complete. This did not reflect additional time involving communica- tions with nonenvironmental functions such as accounting, operations and IT that have a significant investment in the process. With the automation, the process dropped the total work effort to fewer than two man-hours. In addition, the results could easily be validated and produced a considerably more accurate outcome. In subsequent filings, the producer will be able to readily update the previous output stored in the central data warehouse with little review necessary inter- nally to complete the process. The producer estimated that the cost savings going forward could approximate $1 million per year just with the reduced reporting regulatory effort. With the creation of the cutting-edge and validated soft- ware calculations to accomplish the task already created, subsequent efforts will dramatically reduce the manpower needed. At the same time, the company will satisfy the EPA's requirement for maintaining a transparent, historical re- cord. Figure 2 shows an emissions comparison summary of previous and current reporting periods. Beyond the GHG reporting function, operations man- agement has already benefitted by being able to see the larger picture of its asset. They are now able to have a total under- (Image courtesy of Wood Group) FIGURE 2: PRIOR YEAR AND REPORTING YEAR EMISSION COMPARISON

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