Schlumberger’s innovative AI approach to well completion optimization
In the oil and gas sector, optimizing performance and managing resources is crucial. Schlumberger, a renowned name in the industry, addresses this problem with an innovative software tool that predicts filling fluid needs for post-extraction. These predictions help them use fluid efficiently by reducing procurement and disposal costs. The tool uses data analytics to help operators make better decisions, streamline operations, and manage resources more effectively.
Oil extraction often requires fluids for hydraulic fracturing, cooling, and maintaining reservoir pressure. The amount needed varies based on reservoir type, extraction technology, and geological conditions.Hence, the challenges were to collect and integrate data from various sources, including well logs, drilling reports, and real-time sensor data. Identifying complex patterns and correlations in the data to make accurate predictions. Accounting for the variability in well characteristics and geological conditions. Ensuring optimal fluid usage without compromising the efficiency of the extraction process.
Collaborating with Schlumberger, we developed a sophisticated well optimization tool tailored to the industry’s requirements. Leveraging a team of AI and ML experts, Schlumberger incorporated innovative solutions to enhance operational efficiency and resource management in the oil and gas sector.
We began with a conversational AI that collects user inputs like name, industry, and campaign type to deliver industry-standard optimization and analytics.
Later created algorithms to understand and predict how well subject lines would perform based on past data and the Flesch-Kincaid grade level meter. We also built an easy-to-use dashboard where marketers could input their subject lines, get immediate scores, and see suggestions for improvement. This dashboard aimed to help marketers come up with effective and result-oriented subject lines.
Another valuable addition is the 'Word Cloud feature, offering marketers a curated list of relevant words used in past campaigns, aiding them in optimizing their current campaign strategies. We made sure the SLO tool worked perfectly with HCL Unica’s system, ensuring a smooth experience for users.
We began with a conversational AI that collects user inputs like name, industry, and campaign type to deliver industry-standard optimization and analytics.
Later created algorithms to understand and predict how well subject lines would perform based on past data and the Flesch-Kincaid grade level meter. We also built an easy-to-use dashboard where marketers could input their subject lines, get immediate scores, and see suggestions for improvement. This dashboard aimed to help marketers come up with effective and result-oriented subject lines.
Another valuable addition is the 'Word Cloud feature, offering marketers a curated list of relevant words used in past campaigns, aiding them in optimizing their current campaign strategies. We made sure the SLO tool worked perfectly with HCL Unica’s system, ensuring a smooth experience for users.
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