摘要：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 is 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 unveil the influencing hierarchy of the parameters involved in driving the process within major rock categories – sandstone and carbonate – to regulate a target functionality. The functionalities considered evolve around maximizing the oil recovery, minimizing permeability impairment/ scale damage. A pool of experimental as well as field data was used for this sake, 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 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 – evidenced by the minimal errors in the bootstrapped results. Doi: 10.28991/HIJ-2021-02-03-05 Full Text: PDF
