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to address high-stakes scenarios . A hybrid causal AI model , combining data and SME knowledge , can handle more extreme situations with less data , addressing outliers rather than discarding them .
Still , a causal AI model cannot predict everything . Acknowledging that some situations are too critical to be wrong means that the AI must admit when it does not know the answer .
Causal AI in practice
With the goal of estimating gas / oil ratio ( GOR ) using mud gas logs and other data available during drilling , Chevron piloted Senslytics ’ CausX AI , a causal AI model that used a multi-view approach . Views were created based on hydrocarbon ratios , depth and pressure , and geologic age . If the views do not agree , Senslytics ’ CausX AI platform tells the engineer that it is “ unable to interpret the data .” In the example to the right , we were
able to outperform three existing methods for estimating GOR . You ’ ll notice in Well 4 , there are no points on the graph for any of the GOR estimation methods . For Formulas 1 , 2 , and 3 , there are no points because the results were off by over 80 percent . In contrast , the CausX AI model recognized it could not interpret the data . In this case , technical experts could focus on one well ( Well 4 ) that needed their expert insight , instead of dividing their attention and efforts across nine wells .
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