The role of water in the laboratory thermal advancement of immature type I kerogen from the Cretaceous Qingshankou Formation in China.

To understand variations in geochemistry, organic petrology, and chemical composition of crude oil and byproducts, an immature sample from the Cretaceous Qingshankou Formation in the Songliao Basin, China, was analyzed by anhydrous and hydrous pyrolysis (AHP/HP) at a wide range of temperatures ranging from 300 °C to 450 °C. The geochemical parameters: TOC, S2, HI, and Tmax obtained from Rock-Eval pyrolysis showed both a decrease and an increase as thermal maturity progressed under HP and AHP conditions. Gas chromatography (GC) analysis showed the presence of n-alkanes in the C14 to C36 range in both expelled and residual byproducts, a Delta-shaped configuration although many samples had a gradually reducing (tapering) trend toward the high range. Gas chromatography-mass spectrometry (GC-MS) analysis revealed both an increase and a decrease in biomarker and very small changes in aromatic compound variations with increasing temperature during pyrolysis. To be more specific, C29Ts biomarker increased with temperature for the expelled byproduct, while the opposite trend was observed for the residual one. Next, The Ts/Tm ratio initially increased and then decreased with temperature while the C29H/C30H ratio fluctuated for the expelled byproduct but increased for the residual. Moreover, the GI and C30 rearranged hopane to C30 hopane ratio remained unchanged whereas the C23 tricyclic terpane/C24 tetracyclic terpane ratio and the C23/C24 tricyclic terpane ratio showed varying trends with maturity alike the C19/C23 and C20/C23 tricyclic terpane. Ultimately, based on organic petrography observations, increasing the temperature resulted in higher bitumen reflectance (%Bro, r) and optical and structural alterations in the macerals. The findings of this study provide valuable insights for future exploration endeavors in the studied region. Moreover, they contribute to our understanding of the significant role of water in the generation and expulsion of petroleum and associated byproducts, thereby facilitating the development of updated models in this field.

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.

Oil Family Typing Using a Hybrid Model of Self-Organizing Maps and Artificial Neural Networks.

Identifying the number of oil families in petroleum basins provides practical and valuable information in petroleum geochemistry studies from exploration to development. Oil family grouping helps us track migration pathways, identify the number of active source rock(s), and examine the reservoir continuity. To date, almost in all oil family typing studies, common statistical methods such as principal component analysis (PCA) and hierarchical clustering analysis (HCA) have been used. However, there is no publication regarding using artificial neural networks (ANNs) for examining the oil families in petroleum basins. Hence, oil family typing requires novel and not overused and common techniques. This paper is the first report of oil family typing using ANNs as robust computational methods. To this end, a self-organization map (SOM) neural network associated with three clustering validity indexes was employed on oil samples belonging to the Iranian part of the Persian Gulf oilfields. For the SOM network, at first, 10 default clusters were selected. Afterward, three effective clustering validity coefficients, namely, Calinski-Harabasz (CH), Silhouette (SH), and Davies-Bouldin (DB), were studied to find the optimum number of clusters. Accordingly, among 10 default clusters, the maximum CH (62) and SH (0.58) were acquired for 4 clusters. Similarly, the lowest DB (0.8) was obtained for four clusters. Thus, all three validation coefficients introduced four clusters as the optimum number of clusters or oil families. According to the geochemical parameters, it can be deduced that the corresponding source rocks of four oil families have been deposited in a marine carbonate depositional environment under dysoxic-anoxic conditions. However, oil families show some differences based on geochemical data. The number of oil families identified in the present report is consistent with those previously reported by other researchers in the same study area. However, the techniques used in the present paper, which have not been implemented so far, can be introduced as more straightforward for clustering purposes in oil family typing than those of common and overused methods of PCA and HCA.