Reconstructing the Cenozoic environmental history of Hetao Basin, in the northern part of the Ordos Plateau in North China, is important not only for revealing the evolution of the Yellow River, but also for understanding the formation of the Hobq Desert. Here we present the stratigraphic framework of drill core DR01 with length of 2503.18 m, and the results of magnetostratigraphic and ESR dating and multi-proxy analyses of drill core WEDP05 with length of 274.60 m, from the Hetao Basin. The magnetostratigraphic and ESR results indicate that core WEDP05 spans the last ~1.68 Ma. Stratigraphic sequence of core DR01 indicates that the Hetao area was uplifted and eroded during the early Cenozoic, before subsiding to form a sedimentary basin. Subsequently, the basin was a fluvio-lacustrine environment during the Pliocene and then experienced alternating desert and fluvio-lacustrine conditions during the Quaternary. Sedimentary facies and multi environmental-proxy analyses of core WEDP05 indicate that the basin was occupied by a fluvio-lacustrine system during the following intervals: ~1.47-~1.30 Ma, ~1.17-~1.07 Ma, ~0.68-~0.60 Ma and from ~0.47 Ma to the last interglacial; and that a desert environment developed during the lake regression phases of ~1.30-~1.17 Ma, ~1.07-~0.68 Ma and ~0.60-~0.47 Ma. The presence of aeolian sand at the base of core WEDP05 suggests that the origin of the Hobq Desert can be traced back to the early Pleistocene, and resulted from the erosion and transportation of exposed fluvio-lacustrine sediments by near-surface winds associated with the Asian winter monsoon. A large river channel in the Hetao Basin may have existed as early as the Pliocene, which was 3 occupied by the Yellow River when its upper reaches formed by at least ~1.6 Ma. Subsequently, at least since ~1.2 Ma, the Yellow River formed its drainage system around the Hetao Basin and controlled the paleoenvironment evolution of the basin.

In northwestern China, carbonate δ 18 O variation has been closely associated with evaporation and precipitation, whereas the variation of carbonate δ 13 C generally reflects patterns of palaeovegetation. Located within the transitional zone between the Chinese Loess Plateau and the Tibetan Plateau, the Lanzhou Basin has developed a continuous sequence of Cenozoic sediments which have been subjected to detailed sedimentological and high-resolution magnetostratigraphic analyses. In the present study, pedogenic carbonate O and C isotopic analyses were obtained throughout the entire Cenozoic sequence. The δ 18 O record exhibits a general positive trend with several abrupt changes. A dramatic positive shift in the δ 18 O record at ~33 Ma indicates the initiation of the aridification process within the basin, which was likely associated with the late Eocene westward retreat of the Tethys Sea and global cooling. Two significant positive shifts in the δ 18 O record at ~22 Ma and ~3.5 Ma are synchronous with major increases in aeolian dust deposition on the Chinese Loess Plateau and in the North Pacific Ocean, suggesting the intensified aridity of the Asian interior, which is likely related to the stepwise uplift of the Tibetan Plateau via the blocking of water vapour pathways. The δ 13 C values exhibit a weak positive trend with a remarkable shift at ~3.5 Ma. This trend is likely related to a decrease in vegetation density in response to the ongoing Cenozoic aridification, whereas the shift at ~3.5 Ma may reflect the large-scale expansion of C 4 plants.

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Quantitative appraisal of different operating areas and assessment of uncertainty due to reservoir heterogeneities are crucial elements in optimization of production and development strategies in oil sands operations. Although detailed compositional simulators are available for recovery performance evaluation for SAGD, the simulation process is usually deterministic and computationally demanding, and it not quite practical for real-time decision-making and forecasting. Data mining and machine learning algorithms provide efficient modeling alternatives, particularly when the underlying physical relationships between system variables are highly complex, non-linear, and possibly uncertain.In this study, a comprehensive training set encompassing SAGD field data compiled from numerous publicly-available sources is studied. Exploratory data analysis is carried out to interpret and extract relevant attributes describing characteristics associated with reservoir heterogeneities and operating constraints. Because of their ease of implementation and computational efficiency, knowledge-based techniques including artificial neural networks (ANN) are employed to facilitate SAGD production performance prediction. Predicting (input) variables including porosity, net-to-gross ratio, saturation, gross pay, normalized shale barrier thickness and distance to well pair, and initial production rate are formulated. Measures such as cumulative production over discrete time intervals are considered as prediction (output) variables. Data records that are comprised of both input and output variables are assembled; the network is trained using the data set to identify all significant patterns and relationships that exist between the input and the output variables. The model is subsequently validated using a cross-verification procedure, during which records that have been excluded at the training stage are presented to the model. This paper demonstrates that knowledge-based techniques can be implemented in a practical manner to analyze large amount of competitor data efficiently. The approach can be integrated directly into most existing reservoir management routines. It can also be readily updated when new information has become available. Given that robust reservoir management and real-time decision-making are major challenges faced by the industry, the data-driven models presented in this paper has great potential to be applied in other recovery projects such as solvent-aided steam injection.

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Summary Understanding the shear strength of caprock shale and oil sands is important in risk assessment of slope stability in open-pit mining, caprock integrity of in-situ thermal recovery, and optimization of bitumen production from oil sands. A robust and efficient upscaling technique is essential to model the impact of heterogeneity on the deformation and failure of oil sands and caprock shale. Although conventional analytical and numerical upscaling techniques are available, many of these methods consider oversimplified assumptions and have high computational costs, especially when considering the impact of spatially correlated interbedded shales on the shear strength. A machine learning enhanced upscaling (MLEU) technique that leverages the accuracy of local numerical upscaling and the efficiency of artificial neural network (ANN) is proposed here. MLEU uses a fast and accurate ANN proxy model to predict the anisotropic shear strength of heterogeneous oil sands with interbedded shales. The R2 values of the trained ANN models exceed 0.94 for estimating shear strengths in horizontal and vertical directions. The deviation of upscaled shear strength from numerical upscaled results is improved by 12–76% compared with multivariate regression methods like response surface methodology (RSM) and polynomial chaos expansion (PCE). In terms of computational efficiency, the proposed MLEU method can save computational effort by two orders of magnitude compared with numerical upscaling. MLEU provides a reasonable estimate of anisotropic shear strength while considering uncertainties caused by different distributions of shale beddings. With the increasing demand for regional scale modeling of geomechanical problems, the proposed MLEU technique can be extended to other geological settings, where weak beddings play a significant role and the impact of heterogeneity on shear strength is important.

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