________________________________________________________________________________________ Artificial Intelligence
In addition to demonstrating improved accuracy and dependability , causal AI must also provide reasoning behind its interpretation or estimation to establish trust . The logic behind a conclusion should make sense to the engineers and SMEs using the models . This is one of the final key checks to determine whether an AI model ’ s interpretation should be trusted . In the example above , CausX AI would provide an answer such as : “ hydrocarbon ratios from the mud gas log are not consistent with those found in the vicinity . Wells at similar depths , pressure , and geologic age typically have a higher GOR than is estimated by the hydrocarbon ratios .”
Moving forward
Causal AI has a multitude of potential use cases in energy ( i . e . pipeline corrosion , flow assurance , hydrogen PEM degradation , etc .) and other scientific industries . The challenges of limited data , identifying abnormal behavior , high-stakes decisionmaking , varying delays between cause and effect , and lack of explainability are no longer insurmountable obstacles . Causal AI can address these challenges in a way that increases trust and keeps engineers involved in the decision-making process , while allowing them to focus their time on the most crucial issues . The result is preventing events that are catastrophic financially , environmentally , or to human health with the use of causal AI . ■
Blake Bixler www . senslytics . com
Blake Bixler is CEO of Senslytics and an Energy Tech Advisor for Cortado Ventures . Senslytics is a causal AI start-up that empowers engineers and energy professionals to prevent costly failures that were previously unpredictable , reducing risks and improving capital efficiency .
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