Estimation And Prediction Of Drilling Geologic Characteristic Parameters Based On Multi-source Information Fusion

A great quantity of complex and uncertain factors exist in petroleum exploration and exploitation, especially in the drilling engineering. The different geological conditions, such as the change of lithology, formation drillability and pore pressure, may be encountered at any time during drilling, which can bring complex factors to the drilling process. The inaccurate understanding of the complex drilling environment underground will affect the drilling construction, and even cause drilling accidents and endanger the personal safety. In this paper, first the multi-source information in petroleum exploration and exploitation is analyzed, and then the estimation and prediction of drilling geologic characteristic parameters, such as the formation drillability, pore pressure, formation lithology etc, are emphatically investigated in detail. The main contributions are as follows.The information source and expression pattern in petroleum exploration and exploitation are studied, and then the relation between the multi-source information and the drilling geologic characteristic parameters is analyzed. After that, the multi-dimensional heterogeneous spatial model is established and the multi-source information is characterized with a knowledge expression system.The relation between mud logging, well log data and formation drillability is analyzed, and a novel method for predicting formation drillability based on relevance vector machine (RVM) is proposed. Then the prediction model for formation drillability is established by training the RVM. The quantum particle swarm optimization algorithm (QPSO) and support vector machine (SVM) are combined to form a new information fusion method. Then this method with the multi-dimensional heterogeneous space model is applied to formation drillability prediction.The drilling characteristic parameters related to the drilling penetration rate are extracted with genetic algorithm using the drilling rate model. After that, an abnormal formation pressure detection method based on drilling characteristic parameters is proposed by analyzing the trend of these real-time drilling characteristic parameters. Then a pore pressure monitoring while drilling method is proposed by the analysis of the relation between differential pressure coefficient and the pore pressure according to the extracted drilling characteristic parameters. A modified Fillippone pore pressure prediction model is proposed through the analysis of the relation between seismic interval velocity and pore pressure. Firstly, the parameter in the model is initialized according to the region geological information and the data of drilled well. Then the acquired pore pressure data and the mud weight information while drilling are incorporated into the model as the constraint, which makes it possible to update the parameters in the model with Bayesian inference method in real time. Thereby, the pore pressure prediction accuracy is increased and the uncertainty of the prediction is reduced at the same time.To address the well log lithology identification issue accurately, a novel modified Adaboost combined with SVM method is proposed. First, the kernel principal component analysis algorithm is utilized to extract the feature of the actual well log data. Then the support vector machine is trained with the extracted feature and lithologic profile data. After that, the lithology identification model is established based on Adaboost-SVM by boosting a number of weak support vector machine classifiers with Adaboost algorithm. Finally, the lithology identification results with Adaboost-SVM algorithm are compared with the results using QPSO-SVM algorithm.By analyzing the advantages and disadvantages of each method based on Walsh transform or Gaussian model for automatic segmentation of well logs respectively, a method for joint application of the Walsh transform and Gaussian model for automatic segmentation of multiple well logs is proposed. On one hand, the method maintains the feature of simplicity and rapidity of Walsh transform. On the other hand, it adjusts the segmentation boundary with Walsh transform by Gaussian model. Thereby, a coarse-to-fine segmentation of well logs can be achieved. The experimental results show that the proposed method can better perform the segmentation with multiple well logs and has higher practicability and reliability.Based on the combination of support vector machine and the semivariogram, a spatial interpolation method is proposed in the paper, which can be used to the drilling characteristic parameters interpolation estimation. Considering the semivariogram as the constraint of the objective function of support vector machine, this method can not only make use of the spatial correlation structure reconstruction ability of semivariogram, but also retain the strong nonlinear regression ability of support vector machine. The attribute correlation and spatial correlation of the spatial variation are taken into consideration simultaneously, which results in better interpolation results.