To get timely and actionable information about reservoir pressure, temperature, flow, and acoustics changes to control their operations and improve reservoir performance and profitability.
FREMONT, CA: Oil and gas firms are continually confronted with various industry-specific obstacles, such as a lack of visibility into complicated operational processes, performance improvement concerns, equipment life cycle management, logistics complexity, and compliance with environmental standards. Examine the ever-increasing amount of data created by oil and gas firms to overcome these obstacles when transformed into actionable insights. Big data analytics helps streamline essential oil and gas processes in the upstream, midstream, and downstream sectors, including exploration, drilling, production, and delivery.
Upstream sector
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Organize seismic information
Collecting seismic data (acquired with sensors) across a possible area of interest in search of petroleum sources is the first step in upstream analytics. After the data has been collected, it is processed and analyzed to select a drilling location. For assessing the amount of oil and gas in oil reservoirs, seismic data is merged with other data sets (a company's historical information on previous drilling operations, research data, and so on).
Drilling processes are optimized
Customizing predictive models that detect probable equipment breakdowns is one method to improve drilling procedures. The equipment is equipped with sensors to collect data during drilling operations as a starting point. Machine learning algorithms are used to analyze this data and equipment metadata to detect usage patterns that are likely to result in breakdowns.
Make improvements to reservoir engineering
Temperature, acoustic, pressure, and other downhole sensors can collect data that helps companies increase reservoir output. Companies can use big data analytics to develop reservoir management applications.
Midstream sector
The petroleum industry's logistics are highly complicated, and the main focus is transporting oil and gas with the least amount of risk feasible. Sensor analytics is used by businesses to ensure the safe logistics of their energy products. Predictive maintenance software examines sensor data from pipelines and tankers to detect anomalies allowing accidents to be avoided.
Downstream sector
Oil and gas companies can use big data predictive analytics to reduce refining equipment downtime and maintenance costs. The equipment's performance is assessed by comparing historical and present operational data. The performance forecast is fine-tuned based on the device's end-of-life criteria and failure conditions. Finally, the estimated equipment performance is displayed and communicated to maintenance specialists to make decisions such as whether or not to replace the asset.