致密油储层甜点地震预测AbstractIn this paper, we propose a comprehensive method for predicting sweet spots in tight oil reservoirs using seismic data. The proposed method utilizes advanced seismic processing techniques, including amplitude variation with offset (AVO) analysis, normalized impedance inversion (NII), and spectral decomposition, to identify the seismic attributes that are most indicative of the sweet spots. Based on these attributes, we train a machine learning algorithm to predict sweet spots in the target reservoir with high accuracy. To verify the effectiveness of our method, we conduct a case study using a dataset from a tight oil reservoir in Western Canada. Our results show that our method can accurately predict sweet spots and provide valuable insights for reservoir management and optimization.IntroductionTight oil reservoirs are a type of unconventional reservoir with low permeability and porosity, making it challenging to extract hydrocarbons from these reservoirs. In recent years, exploration and development activities in tight oil reservoirs have increased due to the depletion of conventional oil reserves. However, it is crucial to identify the sweet spots in these reservoirs to optimize production and recovery. Seismic data has been used to infer the geologic characteristics of these reservoirs, but traditional seismic interpretation techniques have limited effectiveness in predicting sweet spots. Therefore, it is necessary to develop a more advanced prediction method to identify sweet spots in tight oil reservoirs effectively.MethodologyOur proposed method consists of three main steps: seismic data processing, feature selection, and machine learning modeling.Seismic Data ProcessingWe start by processing the seismic data to enhance the detectability of the sweet spots. We use AVO analysis to identify the acoustic impedance contrast between the reservoir and the surrounding rocks. We also apply NII to extract the seismic attributes that are most indicative of the sweet spots. Lastly, we use spectral decomposition to identify the frequency band that corresponds to the sweet spots.Feature SelectionNext, we use feature selection techniques to identify the most essential seismic attributes that are most indicative of the sweet spots. We use a combination of statistical analysis and machine learning algorithms to select the features that have the highest correlation with the sweet spots.Machine Learning ModelingFinally, we use a machine learning algorithm to predict sweet spots in the target reservoir. We use a supervised learning approach and train a model using the selected seismic attributes as input and sweet spots as the output. We use a random forest algorithm due to its ability to handle complex, non-linear relationships between different features.Case StudyTo verify the effectiveness of our proposed method, we conduct a case study using a dataset from a tight oil reservoir in Western Canada. We use well logs and production data to identify the locations of the sweet spots and compare them with the locations predicted by our method.Our results show that our proposed method can predict sweet spots with high accuracy. The predicted sweet spots overlapped well with the actual sweet spots identified from the well logs and production data. Our approach also provided additional insights into the reservoirs geology and provided valuable information for reservoir management and optimization.ConclusionWe propose a comprehensive method for predicting sweet spots in tight oil reservoirs using seismic data. Our approach uses advanced seismic processing techniques, feature selection, and machine learning algorithms to identify the most indicative seismic attributes and predict sweet spots accurately. Our case study results show that our method is effective in identifying sweet spots and can provide valuable information for reservoir management and optimization.In addition to the traditional seismic interpretation techniques, more advanced methods have been developed in recent years to identify and predict sweet spots in tight oil reservoirs. Some researchers have used machine learning algorithms to predict sweet spots, while others have developed workflows that combine different seismic attributes to identify sweet spots.The proposed method in this paper combines several advanced seismic processing techniques and machine learning algorithms to predict sweet spots in tight oil reservoirs. By using AVO analysis, NII, and spectral decomposition, the seismic data are processed to enhance the detectability of the sweet spots. Feature selection techniques are then used to identify the most indicative seismic attributes, which are used to train a machine learning model to predict sweet spots.The case study conducted in this paper shows that the proposed method can predict sweet spots in tight oil reservoirs with high accuracy. The method p