An advanced computational intelligent framework to predict shear sonic velocity with application to mechanical rock classification

Shear sonic wave velocity (Vs) has a wide variety of implications, from reservoir management and development to geomechanical and geophysical studies. In the current study, two approaches were adopted to predict shear sonic wave velocities (Vs) from several petrophysical well logs, including gamma ray (GR), density (RHOB), neutron (NPHI), and compressional sonic wave velocity (Vp). For this purpose, five intelligent models of random forest (RF), extra tree (ET), Gaussian process regression (GPR), and the integration of adaptive neuro fuzzy inference system (ANFIS) with differential evolution (DE) and imperialist competitive algorithm (ICA) optimizers were implemented. In the first approach, the target was estimated based only on Vp, and the second scenario predicted Vs from the integration of Vp, GR, RHOB, and NPHI inputs. In each scenario, 8061 data points belonging to an oilfield located in the southwest of Iran were investigated. The ET model showed a lower average absolute percent relative error (AAPRE) compared to other models for both approaches. Considering the first approach in which the Vp was the only input, the obtained AAPRE values for RF, ET, GPR, ANFIS + DE, and ANFIS + ICA models are 1.54%, 1.34%, 1.54%, 1.56%, and 1.57%, respectively. In the second scenario, the achieved AAPRE values for RF, ET, GPR, ANFIS + DE, and ANFIS + ICA models are 1.25%, 1.03%, 1.16%, 1.63%, and 1.49%, respectively. The Williams plot proved the validity of both one-input and four-inputs ET model. Regarding the ET model constructed based on only one variable,Williams plot interestingly showed that all 8061 data points are valid data. Also, the outcome of the Leverage approach for the ET model designed with four inputs highlighted that there are only 240 "out of leverage" data sets. In addition, only 169 data are suspected. Also, the sensitivity analysis results typified that the Vp has a higher effect on the target parameter (Vs) than other implemented inputs. Overall, the second scenario demonstrated more satisfactory Vs predictions due to the lower obtained errors of its developed models. Finally, the two ET models with the linear regression model, which is of high interest to the industry, were applied to diagnose candidate layers along the formation for hydraulic fracturing. While the linear regression model fails to accurately trace variations of rock properties, the intelligent models successfully detect brittle intervals consistent with field measurements

Hydrocarbon source rocks in Kazhdumi and Pabdeh formations—a quick outlook in Gachsaran oilfield, SW Iran

Geochemical study of Kazhdumi and Pabdeh Formations as potential source rocks in Gachsaran Oilfield demonstrates that the Kazhdumi Formation has a fair to good capability of hydrocarbon generation and predominately contains type II-III kerogen. On the other hand, the Pabdeh Formation has a poor to good petroleum potential and contains different kerogen types, including type II, type II-III, type III and even for one sample, type IV, indicating different depositional conditions for this formation. The geochemical log of the Kazhdumi Formation shows that there is a close correlation between different geological parameters as noticed prominently in well number 55, which suggests the more extensive the anoxic condition, the higher the petroleum potential is for Kazhdumi Formation. By contrast, a poor correlation between TOC and other Rock–Eval-derived parameters for the Pabdeh Formation at a depth of more than 2100 m may demonstrate the inert organic matter and mineral matrix effects at this depth interval. However, biomarkers show differences in lithology and depositional environment for the Kazhdumi Formation in well numbers 55 and 83. On the other hand, the Pabdeh Formation has a mixed lithology (carbonate-shale) deposited in a marine setting under suboxic–anoxic condition. Moreover, thermal maturity indicators suggest that Pabdeh and Kazhdumi Formations are immature and early mature, respectively