Research and application of an intelligent formation drillability assessment method based on multi-source data fusion

Formation drillability assessment is crucial for drilling operations, as it directly influences operational efficiency and cost-effectiveness. Traditional three-dimensional (3D) assessment methods often face challenges due to the unstable integration of multi-source and cross-scale data, resulting in limited spatial generalization and suboptimal prediction performance. To address these limitations, this paper proposes a multi-source data fusion method based on a gated recurrent unit (GRU) network to enhance intelligent formation drillability assessment and improve drilling efficiency in a study area in eastern China. The method consists of two phases: well data training and 3D application. In the first phase, pseudo-depth domain seismic records synthesized from seismic average wavelets and well logging data serve as the foundation. Sensitive attributes related to formation drillability are further extracted as network inputs. These sensitive attributes include a velocity model incorporating geological information and a seismic frequency-fraction attribute that captures multi-scale stratigraphic structure. A corrected drillability index (D_c) is used as a label for model training, ensuring that the network learns to establish an accurate mapping relationship between input attributes and drillability indicators. This training method leverages the temporal and sequential learning capabilities of the GRU network to effectively model complex relationships in the data. In the second phase, the pretrained network was extended to 3D applications, constructing a 3D input dataset by extracting the corresponding attributes. This dataset was then fed into a pretrained GRU model to predict formation drillability in the study area. Analysis of five representative wells in the study area validated the effectiveness of D_c in characterizing rock drillability in the study area. Furthermore, experiments using the Marmousi numerical model demonstrated that the method outperformed traditional intelligent prediction methods, such as those relying solely on raw seismic data or a combination of raw seismic and well logging data. Practical application in the study area further confirmed the method’s ability to effectively capture variations in formation drillability. By providing reliable predictions, the method becomes a powerful tool for optimizing drilling operations and enhancing drilling engineering decision-making.