Risk Based on Data Science Fundamental to Improve ProductionTue, 01/21/2020 - 11:01
Q: What is the main added value that data science can offer an industry like oil and gas?
A: Data science is a multidisciplinary field of knowledge that applies scientific methods and models, as well as processes, algorithms, information technologies and systems, to extract knowledge and insights from structured and unstructured data. Data science can contribute to the improvement of decision-making and risk-control effectiveness. The oil and gas industry, in all its value-chain, is constantly producing a lot of valuable data on upstream, midstream and downstream that is key for understanding the operation, asset behavior and trends, as well as issues related to risks and compliance and conformance with requirements.
Q: How receptive is the Mexican oil and gas market to risk analysis based on data science services?
A: Risks analysis capabilities, effectiveness and efficiency are data-driven and therefore depend on data availability and quality. Usually, companies, even big ones, locally and worldwide, are not aware of the relevance of data and instead they are wasting that value by ignoring their own data, left to its fate in an unsorted bunch of files of different formats and information systems. However, using a business or money-oriented approach, demonstrating that data is money, companies are open to understanding how to make the most of the data.
Q: How does enhanced data collection and analysis help clients’ make smarter, more cost-effective and less risky decisions?
A: Data science, as well as risk analysis and its industry applications, helps oil and gas companies have a better understanding of their investments and operations, benefiting from those vast and complex amounts of data they are already gathering, processing and producing in their day to day activities. Enriched real data should support the risk-based decision-making process regarding actions needed to optimize and secure operations and finances. Data analytics and visualization help organizations to understand their contexts and risks, on one hand, enhancing decisions in order to know where, how and when to put their money to work, and when not to. Also, data facilitate an adequate response for normal conditions, under high demand scenarios like a sudden fall in oil production, as well as for emergency situations like a gas leak from a facility, dealing with the high level of uncertainty in the oil and gas business. Understanding data will allow better asset management, enhancing its availability, adequacy and value generation, protecting companies from losing money.
Q: How can data science help boost production in Mexico’s mature fields?
A: A common challenge in mature fields is how to boost production performance. Historically, many techniques have been applied to understand, analyze and predict production considering different enhanced recovery alternatives and techniques, with a relative low success rate. In such a scenario, data science, as well as datadriven risk analysis of advanced oil and gas production, applied together with known analysis techniques, can enhance production in quantity and quality and revenue. For Mexico, this is the right moment to adopt new strategies to improve production. The first step beyond project management is gathering, structuring and understanding the available data, and enriching it with complementary techniques, using both classic and modern approaches. Oil and gas assets already have a great amount of data that needs to be analyzed; in fact, some mature fields that are almost abandoned or working at minimum output, could even be brought to life by processing and interpreting available data. Finally, data science, and in general digital transformation, as per the 2017 World Economic Forum report, generated US$1.7 trillion worth of value from 2016 through to 2025.
IMS/OHT Global provides specialized knowledge transfer and evaluation services focused on transforming risks, increasing performance and enabling compliance. Its core discipline is industrial and financial risk and control architectures.