A Data Mining Perspective on the Confluent Ions` Effect for Target Functionality
The production of hydrocarbon resources at an oil field is concomitant with challenges with respect to the formation of scale inside the reservoir rock, intricately impairing its permeability and hindering the flow. Historically, the effect of ions has been attributed to the undergone phenomenon; nevertheless, there exists a great deal of ambiguity about its relative significance compared to other factors, or the effectiveness as per the ion type. The present work applies a data mining strategy to uncover the influence hierarchy of the parameters involved in driving the process within major rock categories—sandstone and carbonate—to regulate a target functionality. The functionalities considered revolve around maximizing oil recovery and minimizing permeability impairment/scale damage. A pool of experimental as well as field data was used for this purpose, accumulating the bulk of the available literature data. The methods used for data analysis in the present work included the Bayesian Network, Random Forest, Deep Neural Network, as well as Recursive Partitioning. The results indicate a rolling importance for different ion species, altering under each functionality, which is not ranked as the most influential parameter in either case. For the oil recovery target, our results quantify a distinction between the source of an ion of a single type in terms of its influencing rank in the process. This latter deduction is the first proposal of its kind, suggesting a new perspective for research. Moreover, the machine learning methodology was found to be capable of reliably capturing the data, as evidenced by the minimal errors in the bootstrapped results.
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