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中文摘要：
岩石可钻性是石油钻井中钻头优选、钻进参数制定的重要依据，岩石可钻性钻前预测对于提高深井、超深井钻井效率，降低钻井综合成本具有重要意义。目前，国内外关于新探区野猫井的岩石可钻性钻前预测方面的研究很少。现有评价方法不能满足对缺少测井资料和岩心资料的野猫井的岩石可钻性进行钻前预测的需要。因此，在相似构造选择的基础上，提出了一种对野猫井岩石可钻性进行钻前预测的方法。该方法首先应用已钻相似构造井的地震资料、测井资料以及岩心测试资料，建立岩石可钻性钻前预测遗传神经网络模型，然后将神经网络与遗传算法有机结合起来，以神经网络理论为基础，利用遗传算法优化隐含层神经元个数和网络连接权值，最后利用野猫井的地震资料进行岩石可钻性钻前预测。应用该方法对新疆油田MXI井的岩石可钻性进行了钻前预测，预测结果与测井资料评价结果相比，平均相对误差为9．8％。比较结果表明，该方法具有较高的预测精度，是合理的。
英文摘要：
The rock drillability is an important index for optimizing bit selection and programming drilling parameters. Prediction of rock drillability before drilling is very important for the increase of drilling efficiency and the reduction of drilling cost in deep wells or super-deep wells. Nowadays, at home and abroad, there are few reports about how to predict rock drillability of wildcat well before drilling in new exploration area. For wild well, there are lack of logging data and core data. Therefore, present evaluation methods of rock drillability can not be applied to predict rock drillability of wildcat well before drilling. Based on the selection of similar structure, a method for predicting the rock drillability of wildcat well is proposed. GA-BP （genetic neural network） model is established by use of seismic data, logging data and core data of the similar structure well. On the basis of neural network theory, genetic algorithm is used to optimize neural network. According to seismic data, rock drillability of wildcat well will be predicted before drilling by use of this GA-BP model. Rock drillability for Xinjiang well MX1 is predicted before drilling. Compared with evaluation results of logging data, average relative error of the prediction result is 9.8%. The field application result testifies that this method is feasible and has a high accuracy.
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