Evaluation of oil shale resources based on geochemistry and logging in Tuanyushan, Qaidam Basin, Northwest China

Abstract

In this study, oil shale resources in the Middle Jurassic Shimengou Formation in the Tuanyushan area along the northern margin of the Qaidam Basin in Northwest China are evaluated. The total organic carbon (TOC) content of oil shale in the study area is positively correlated with resistivity and negatively correlated with acoustic travel time and natural gamma. Based on TOC and oil yield, as well as the log response differences between the resistivity logging curve, the natural gamma logging curve and the acoustic travel time logging curve of mudstone with differing TOC contents, ΔlogR and stepwise regression models can be created to quantitatively determine the TOC content of oil shale. The results of the two prediction models and the measured values show the mean absolute deviation (MAD) of the ΔlogR model to be 0.95 wt% and the coefficient of determination, R 2 , 0.67. The MAD of the stepwise regression model is 1.15 wt% and R 2 is 0.54. Of these two prediction models, the ΔlogR model has a higher recognition ability, but the predicted results are easily disturbed by silty mudstone. Therefore, the stepwise regression model is used to quantitatively identify oil shale resources in the study area. The comparison of the predicted and measured oil yields of oil shale from exploratory wells shows their R 2 to be 0.49. This suggests that in the whole study area, oil shale resources can be identified by the logging curve. Based on log interpretation, the volume method is used to estimate the area's oil shale resources. The results show the oil shale resources in this area to total 392 million tons and converted shale oil resources 24 million tons.

Hydrochemical and Isotopic Characteristics and the Spatiotemporal Differences of Surface Water and Groundwater in the Qaidam Basin, China

“…The carbonate rock fractures are well developed, resulting in abundant spring water flow with excellent water quality. In the Qilian Mountain area, the aquifer formations of metamorphic rocks include sandstone, shale, slate, and gneiss, while the carbonate rocks mainly exhibit thick and massive structures with well-developed joint fissures, creating favorable conditions for the storage and migration of groundwater [10]. This also explains the higher concentration of HCO 3 − in the water bodies of mountainous regions, as documented in reference [25].…”

“…Vertically, the transition comprises a single pebble layer to a multilayer structure with overlapping rock types. Horizontally, parallel to the basin's edge mountains, the main river channels form an independent aquifer system in the front faces of mountainous areas [10].…”

“…Some areas are isolated by mountains and are independent units. However, vertically, they generally connect with the unconfined aquifer of the alluvial fan primarily through vertical top-supported cross-flow groundwater recharge, and then the groundwater is discharged via groundwater evaporation [10]. The supply of oilfield water in the southern part of the basin (as shown in Figure 1b) is also a leading cause of higher Cl − content in the water bodies of the southern basin.…”

“…Except for the rivers that are primarily replenished by groundwater, most rivers exhibit seasonal variations. Originating from the surrounding mountainous areas, these rivers either undergo frequent transformations with groundwater, disappear in the desert, or overflow onto the surface before finally draining into a terminal lake [10]. Therefore, the central part of the basin is predominantly an area without runoffs.…”

Chemical Characteristics and Distribution Prediction of Hydrocarbon Source Rocks in the Continental Lacustrine Basin of the Chang 7 Member in the Heshui Area of the Ordos Basin, China

“…The observed difference between these two curves is mainly attributed to the porosity curve's response in rocks abundant in immature organic matter, where oil and gas generation has not yet occurred. In mature hydrocarbon source rocks, in addition to the porosity curve response, hydrocarbons and an increase in resistivity amplify the amplitude difference between these curves [42]. The calculation of ∆LogR is derived from the superposition of sound waves and resistivity, and the equation for ∆LogR is as follows [43]:…”