Data science - time series analysis of oil & gas production in mexican fields

• Published in: Procedia computer science 2022, Vol.200, p.21-30
• Main Authors: Sánchez Morales, María de los Ángeles, Soler Anguiano, Francisca Irene
• Format: Article
• Language: English
• Subjects: ARIMA; Gas; Non-Seasonal Methods; Oil; Production; SARIMA Methods; Seasonal; Time Series Analysis
• ISSN: 1877-0509; 1877-0509
• DOI: 10.1016/j.procs.2022.01.201

Nowadays, large industries of financial, technological, manufacturing, energetic, and service sectors have accomplished the incorporation of Data Science in their operations, processes, and work structures, obtaining significant improvements in their productivity and service potentials. Whereby the oil and gas industry should not be foreign to this science that aids the decision-making processes using the acquired information by extracting massive, structuring data, combining statistics, mathematics, and informatics. Since the variations in petroleum and gas offer and demand are strongly related to the changes in prices, the use of Data Science intends to administer and reduce the risks provoked by the processes and decisions related to every step of the industry’s chain value. Oil and gas exploration, extraction, development, and production generate considerable amounts of data created by different means, lacking order and precision. Hence, data analysis provides formality to the experiments in this area, improving productivity alternatives and creating innovation opportunities. The present paper objective is to comprehend the production fluctuation trends to understand the possible behaviour of future productions in three Mexican fields with the highest petroleum and gas production, Maloob, Zaap, and Ayatzi for petroleum and Ku, Akal, and Maloob for gas, through the application of a Time Series Analysis to each one of them.