Machine learning has a significant advantage for many difficulties in the oil and gas industry, especially when it comes to resolving complex challenges in reservoir characterization. Permeability is one of the most difficult petrophysical parameters to predict using conventional logging techniques. Clarifications of the work flow methodology are presented alongside comprehensive models in this study. The purpose of this study is to provide a more robust technique for predicting permeability; previous studies on the Bazirgan field have attempted to do so, but their estimates have been vague, and the methods they give are obsolete and do not make any concessions to the real or rigid in order to solve the permeability computation. To verify the reliability of training data for zone-by-zone modeling, we split the scenario into two scenarios and applied them to seven wells' worth of data. Moreover, all wellbore intervals were processed, for instance, all five units of Mishrif formation. According to the findings, the more information we have, the more accurate our forecasting model becomes. Multi-resolution graph-based clustering has demonstrated its forecasting stability in two instances by comparing it to the other five machine learning models.

Production logging is used to diagnose well production problems by evaluating the flowprofile, entries of unwanted fluids and downhole flow regimes. Evaluating wells productionperformance can be easily induce from production logs through interpretation of productionlog data to provide velocity profile and contribution of each zone on total production.Production logging results supply information for reservoir modeling, provide data tooptimize the productivity of existing wells and plan drilling and completion strategies forfuture wells. Production logging was carried out in a produce oil well from Mishrif formationof West Qurna field, with the objective to determine the flow profile and fluid contributionsfrom the perforations after the stimulation job.

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