Techniques for improving the water-flooding of oil fields during the high water-cut stage

International audienceThe multi-layer co-exploitation method is often used in offshore oilfields because of the large spacing between the injection and production wells. As oilfields gradually enter the high water-cut stage, the contradiction between the horizontal and vertical directions becomes more prominent, and the distribution of the remaining oil is more complex. Oilfields are facing unprecedented challenges in further enhancing oil recovery. Using oilfield A, which is in the high water-cut stage, as the research object, we compiled a detailed description of the remaining oil during the high water-cut stage using the information collected during the comprehensive adjustment and infilling of the oilfield. In addition various techniques for tapping the potential reservoir, stabilizing the oil, and controlling the water were investigated. A set of key techniques for the continuous improvement of the efficiency of water injection after comprehensive adjustment of high water-cut fields was generated. Based on the determined configuration of the offshore deltaic reservoir, a set of detailed descriptive methods and tapping technology for extracting the remaining oil in the offshore high water-cut oilfield after comprehensive adjustment was established. By considering the equilibrium displacement and using a new quantitative characterization method that includes displacement, a new technique for determining the quantity of water that needs to be injected into a stratified injection well during the high water-cut stage was established. Based on the principle of flow field intensity reconfiguration, a linear, variable-intensity, alternating injection and withdrawal technique was proposed. With the application of this series of techniques, the increase in the water content was controlled to within 1%, the natural reduction rate was controlled to within 9%, and the production increased by 1.060 × 107 m3

Integrating Deep Learning and Hydrodynamic Modeling to Improve the Great Lakes Forecast

The Laurentian Great Lakes, one of the world’s largest surface freshwater systems, pose a modeling challenge in seasonal forecast and climate projection. While physics-based hydrodynamic modeling is a fundamental approach, improving the forecast accuracy remains critical. In recent years, machine learning (ML) has quickly emerged in geoscience applications, but its application to the Great Lakes hydrodynamic prediction is still in its early stages. This work is the first one to explore a deep learning approach to predicting spatiotemporal distributions of the lake surface temperature (LST) in the Great Lakes. Our study shows that the Long Short-Term Memory (LSTM) neural network, trained with the limited data from hypothetical monitoring networks, can provide consistent and robust performance. The LSTM prediction captured the LST spatiotemporal variabilities across the five Great Lakes well, suggesting an effective and efficient way for monitoring network design in assisting the ML-based forecast. Furthermore, we employed an explainable artificial intelligence (XAI) technique named SHapley Additive exPlanations (SHAP) to uncover how the features impact the LSTM prediction. Our XAI analysis shows air temperature is the most influential feature for predicting LST in the trained LSTM. The relatively large bias in the LSTM prediction during the spring and fall was associated with substantial heterogeneity of air temperature during the two seasons. In contrast, the physics-based hydrodynamic model performed better in spring and fall yet exhibited relatively large biases during the summer stratification period. Finally, we developed a statistical integration of the hydrodynamic modeling and deep learning results based on the Best Linear Unbiased Estimator (BLUE). The integration further enhanced prediction accuracy, suggesting its potential for next-generation Great Lakes forecast systems

Integrating Deep Learning and Hydrodynamic Modeling to Improve the Great Lakes Forecast

The Laurentian Great Lakes, one of the world’s largest surface freshwater systems, pose a modeling challenge in seasonal forecast and climate projection. While physics-based hydrodynamic modeling is a fundamental approach, improving the forecast accuracy remains critical. In recent years, machine learning (ML) has quickly emerged in geoscience applications, but its application to the Great Lakes hydrodynamic prediction is still in its early stages. This work is the first one to explore a deep learning approach to predicting spatiotemporal distributions of the lake surface temperature (LST) in the Great Lakes. Our study shows that the Long Short-Term Memory (LSTM) neural network, trained with the limited data from hypothetical monitoring networks, can provide consistent and robust performance. The LSTM prediction captured the LST spatiotemporal variabilities across the five Great Lakes well, suggesting an effective and efficient way for monitoring network design in assisting the ML-based forecast. Furthermore, we employed an explainable artificial intelligence (XAI) technique named SHapley Additive exPlanations (SHAP) to uncover how the features impact the LSTM prediction. Our XAI analysis shows air temperature is the most influential feature for predicting LST in the trained LSTM. The relatively large bias in the LSTM prediction during the spring and fall was associated with substantial heterogeneity of air temperature during the two seasons. In contrast, the physics-based hydrodynamic model performed better in spring and fall yet exhibited relatively large biases during the summer stratification period. Finally, we developed a statistical integration of the hydrodynamic modeling and deep learning results based on the Best Linear Unbiased Estimator (BLUE). The integration further enhanced prediction accuracy, suggesting its potential for next-generation Great Lakes forecast systems

Reducing the gap between streaming and non-streaming Transducer-based ASR by adaptive two-stage knowledge distillation

Transducer is one of the mainstream frameworks for streaming speech recognition. There is a performance gap between the streaming and non-streaming transducer models due to limited context. To reduce this gap, an effective way is to ensure that their hidden and output distributions are consistent, which can be achieved by hierarchical knowledge distillation. However, it is difficult to ensure the distribution consistency simultaneously because the learning of the output distribution depends on the hidden one. In this paper, we propose an adaptive two-stage knowledge distillation method consisting of hidden layer learning and output layer learning. In the former stage, we learn hidden representation with full context by applying mean square error loss function. In the latter stage, we design a power transformation based adaptive smoothness method to learn stable output distribution. It achieved 19\% relative reduction in word error rate, and a faster response for the first token compared with the original streaming model in LibriSpeech corpus

Defective Expression of Mitochondrial, Vacuolar H+-ATPase and Histone Genes in a C. elegans Model of SMA

Spinal muscular atrophy (SMA) is a severe motor neuron degenerative disease caused by loss-of-function mutations in the survival motor neuron gene SMN1. It is widely posited that defective gene expression underlies SMA. However, the identities of these affected genes remain to be elucidated. By analyzing the transcriptome of a Caenorhabditis elegans SMA model at the pre-symptomatic stage, we found that the expression of numerous nuclear encoded mitochondrial genes and vacuolar H+-ATPase genes was significantly down-regulated, while that of histone genes was significantly up-regulated. We previously showed that the uaf-1 gene, encoding key splicing factor U2AF large subunit, could affect the behavior and lifespan of smn-1 mutants. Here, we found that smn-1 and uaf-1 interact to affect the recognition of 3′ and 5′ splice sites in a gene-specific manner. Altogether, our results suggest a functional interaction between smn-1 and uaf-1 in affecting RNA splicing and a potential effect of smn-1 on the expression of mitochondrial and histone genes

Stress Distribution and Fluctuation Cycle on the Rack Face of the Rock Cutting Tool

The distribution of the stress field on the rack face has significant impacts on the performance and service life of the rock cutting tool. A dynamic simulation model of the stress on the rock cutting tool is established by finite element code Abaqus, and the distribution of local stress on the rack face and its impact factors are studied. It is concluded that the local stress on the rack face of the rock cutting tool shows obvious periodical fluctuation characteristics, and the fluctuation cycle of each point on the tool remains unchanged under the same cutting conditions. The stress fluctuation cycle period decreases with the increase of cutting speed inversely. The cutting depth and the back angle of the cutting tool have no obvious impact on the stress fluctuation period. However, the cutting depth and the back angle have obvious impacts on the average stress distributions of each point on the rack face of the tool. That is, the increase of back angle and cutting depth could cause the maximum stress point of the rack face to move upward to the tool tip

Re-Evaluation of Oil Bearing for Wells with Long Production Histories in Low Permeability Reservoirs Using Data-Driven Models

The re-evaluation of oil-bearing wells enables finding potential oil-bearing areas and estimating the results of well logging. The re-evaluation of oil bearing is one of the key procedures for guiding the development of lower production wells with long-term production histories. However, there are many limitations to traditional oil-bearing assessment due to low resolution and excessive reliance on geological expert experience, which may lead to inaccurate and uncertain predictions. Based on information gain, three data-driven models were established in this paper to re-evaluate the oil bearing of long-term production wells. The results indicated that the RF model performed best with an accuracy of 95.07%, while the prediction capability of the neural network model was the worst, with only 79.8% accuracy. Moreover, an integrated model was explored to improve model accuracy. Compared with the neural network, support vector machine, and random forest models, the accuracy of the fusion model was improved by 20.9%, 8.5%, and 1.4%, which indicated that the integrated model assisted in enhancing the accuracy of oil-bearing prediction. Combined with the long-term production characteristics of oil wells in the actual oil field, the potential target sweet spot was found, providing theoretical guidance for the effective development of lower production wells in the late period of oilfield development