2.1 Overview

The study proposes a real-time lithology identification method and develops the CSTT system and smart drilling rig to support this approach, as shown in Figure 1. The CSTT system features automatic cuttings sampling, image acquisition, analysis, and transport functions, and it works in coordination with the smart drilling rig. It collects and analyzes the lithology of the cuttings to identify formation information. The smart drilling rig performs automated drilling operations and, at specific stages of the drilling process, sends sampling commands to the CSTT system as per the preprogrammed procedures. After the identification is completed, the system transmits the identification results to the smart drilling rig, which are displayed on the rig's control system screens and used as the basis for the next stage of drilling.

In the initial phase, several demonstration boreholes need to be drilled to collect sufficient data for training the machine learning model. The trajectory planning of the demonstration boreholes should cover typical formations and potential geological structures that need to be identified later in the mining area. During drilling, the smart drill rig coordinates with the CSTT system to collect cuttings image data at specific stages according to a predesigned frequency. The raw images of collected drill cuttings were preprocessed using a preprocessing method for drill cuttings particle images proposed in this paper. The preprocessed drill cuttings images are then labeled based on borehole resurvey data and logging data. Using transfer learning, the pretrained deep learning network is further trained to build a lithology identification model. It then undergoes an iterative process of training, adjustment, and evaluation to ensure accurate classification of the drill cuttings image samples. Logging and borehole re-survey data are used to validate and optimize the model. At this stage, a soft voting ensemble learning method based on the multitraining model is applied to enhance the model's feature extraction capability from the cuttings, ensuring high identification accuracy.

After completing the demonstration boreholes and the testing and optimization of the model, it will be integrated into the control system of a smart drilling rig. Underground drilling tests will then be conducted to fully verify the reliability and accuracy of the system. Finally, the qualified model will be scaled and integrated into multiple smart drilling rigs to enable real-time identification and recording of formation changes during drilling operations. The data flow of the lithology identification method is shown in Figure 2. The functions of the main equipment in the system are shown in Figure 3.

2.2 The CSTT system

Obtaining formation information by analyzing the shape, color, composition, and weight of drill cuttings is one of the most direct and effective methods. The realization of automatic sampling and analysis of drill cuttings is conducive to improving the automation of the supporting operations of drilling construction and improving the timeliness and accuracy of stratigraphic information acquisition.

2.2.1 Automated sampling testing and transporting of drill cuttings

In this study, a CSTT system was developed, as shown in Figure 4. The system can be deeply integrated and works in coordination with the smart drilling rig to realize the collection of drill cuttings, automatic sampling, weighing, image acquisition, and lithology analysis. Additionally, it can help with long-distance transportation of drill cuttings.

The CSTT system consists of a flow meter, a drill cuttings collection device, an image capture device, a sampling robotic arm, a weighing device, and a drill cuttings conveyor. The flow meter is installed on the drill rig to monitor the flow rate of the drilling fluid entering the borehole. The drill cuttings collection device is installed at the head of the borehole to collect the drilling fluid and cuttings flowing out of the borehole, which are then transported through pipes to the drill cuttings detection and transport device. A sampling robot arm collects the drill cuttings samples and sends them to an image acquisition device for image sampling. The captured images are sent to a data processing unit to analyze their lithology and store them. The weighing device is used to obtain drill cuttings weight information. The drill cuttings conveyor is responsible for the conveying of drill cuttings.

2.2.2 Consistency in sampling timing

Accurate control of drill cuttings sampling locations is the foundation for spatial correspondence between lithology identification results and sampling locations. The precise control of sampling position and sampling timing is realized by deeply integrating the CSTT system and the smart drilling rig. The workflow of the CSTT system is illustrated in Figure 5. When the system is activated, the CSTT system first establishes communication with the smart drill rig control system. The drilling process detection system consists of various sensors responsible for monitoring the drill rig's operational status and determining when drill cuttings sampling is needed.

2.2.3 Consistency of the image acquisition environment

The high humidity, dust, and darkness of the deep underground environment can degrade the quality of acquired drill cuttings images. To ensure consistency in the imaging environment, an image capture device was developed, as shown in Figure 6. This device creates a relatively enclosed environment between the camera and the sampling tray and provides independent lighting to eliminate the impact of ambient light. In addition, the camera uses the same shooting parameters for each capture, such as the shooting environment, shooting angle, camera settings, and exposure time, further ensuring consistency in the drill cuttings image sampling environment.

2.2.4 Methods to prevent mixing of drill cuttings from different formations

To minimize the impact of mixed cuttings from different formations on identification accuracy during the drill cuttings image collection process, this study proposes the following methods: After each drill rod has been completely drilled into the strata, the drilling rig is maintained in a rotating state for a period to thoroughly flush the borehole with drilling fluid, expelling any residual cuttings inside the borehole. To avoid the mixing of cuttings from different layers due to the on-off water operations during the installation of new drill rods, the timing of cuttings sampling is chosen when the half-length of each drill rod has been drilled into the borehole. After capturing the image of the cuttings in the sampling tray, the tray is flipped over by using the flipping device, as shown in Figure 7, before proceeding to the next sampling process, allowing the cuttings inside the tray to be poured into the weighing device below. Even if there is a small amount of residual drill cuttings in the sampling tray, it will be covered by the new inflow of drill cuttings and will not be captured by the image acquisition device.

2.3 Preprocessing method for drill cuttings particle images

The raw data collected are high-resolution images of drill cuttings. Traditional methods, when using transfer learning to train a pretrained network, require resizing the images to the desired dimensions, such as 224 pixels by 224 pixels. This resizing can lead to a significant loss of valuable information. Since drill cuttings are usually a mixture of crushed particles of different materials, knowing the proportion of different materials in the drill cuttings is more meaningful for obtaining the exact location of the stratigraphic interface and guiding the drilling operation.

2.3.1 Preprocessing methods for model training data

To meet the image size requirements of the machine learning model and to capture the proportion of different lithology samples in the drill cuttings, the original drill cuttings image will be cropped in the preprocessing stage to divide it into multiple blocks of the desired size for the model while removing extraneous information such as container edges. Each block after cropping will be numbered sequentially, as shown in Figure 8.

After cropping the images, the next step is to label the lithology of each image. In this step, we use specialized borehole measurement equipment to resurvey the boreholes. These results will be used in conjunction with borehole logging data and known geologic information to obtain formation lithology information at the location where each drill cuttings image was acquired. This lithology information will be used to label the lithology of the image blocks, as shown in Figure 9. The image blocks are then divided into different groups based on lithology to form the model training data set.

Notably, before cropping and labeling the original images, drill cuttings image samples collected from the interface of two geological layers were excluded from the training data set. This step was taken to reduce the impact of mixed cuttings on the lithology labeling accuracy of the training samples.

There is significant variation in the size and orientation of rock particles within cuttings images. It is impossible for the training image set to cover all possible scenarios. Therefore, data augmentation (including image enhancement, image transformation, etc.) is commonly used to increase the number of training samples. By applying various transformations to the images, we can simulate changes in the field environment and expand the recognition model's adaptability. To avoid increasing the storage space required for the data set, runtime augmentation is typically employed. This approach involves applying a certain transformation or combination of transformations to an original training image with a given probability when it is read. The transformed image is then added to the current training batch for model training. The transformed image is not saved after the training session concludes.

Based on the characteristics of drill cuttings images, it is essential to ensure that the transformed images still retain the fundamental features of the drill cuttings. Therefore, only a subset of effective transformation functions is selected. With an 80% probability, an image undergoes one or more of the following geometric transformations: horizontal flip, vertical flip, rotation, and translation. The parameters for these transformations are randomly selected within a specified range, as shown in Table 1.

Table 1. Augmentation options.

Method	Value
Random reflection axis	X and Y
Random rotation (°)	Min:	−180	Max:	180
Random rescaling	Min:	0.5	Max:	2.0
Random horizontal translation (pixels)	Min:	−100	Max:	100
Random vertical translation (pixels)	Min:	−100	Max:	100

2.3.2 Preprocessing methods for normal construction data

After the model is trained, optimized, and integrated into the drilling rig system, the preprocessing method for raw drill cuttings images is slightly different during normal drilling operations. The drill cuttings samples consist of irregular rock particles with randomly distributed orientations. To enable the recognition model to comprehensively extract features from the drill cuttings images, the collected image data are cropped, as shown in Figure 10. Each image block is randomly rotated, creating multiple new image blocks. These newly generated image blocks, along with the original image block, form the lithology testing data set for that specific image block.

The lithology identification result for each image block was determined by selecting the class with the highest weighted average probability in the test data set. The proportion of image blocks with the same lithology type, relative to the total number of blocks, was used to determine the distribution ratio of that lithology within the drill cuttings.

2.4 Soft voting ensemble learning method based on multitraining models

Training machine learning models is a stochastic process, which means that each training run may yield a different model. This randomness causes variations in identification accuracy when applied to different test datasets. Some models may excel in identifying certain features of the drill cuttings images, while others may struggle. To further enhance identification accuracy, this study utilizes the soft voting ensemble learning algorithm to create a multiclassifier system, as depicted in Figure 11.

3.1 The CSTT system test results

In March 2024, a field test of the system was conducted at an underground coal mine in Huainan China. The borehole design and geological information of the construction area are shown in Figure 12. The test site for the system is shown in Figure 13. The boreholes were drilled from the gas control roadway, passing through multiple strata to extract gas from the bottom 11-3 and 11-2 coal seams and adjacent layers.

The construction site was on a roadway above the 11-3 coal seams. The normal distance from the roadway floor to the top of the 11-2 coal seam was approximately 21.4 m. The strata above the 11-2 coal seam included, sequentially, a sandy mudstone layer approximately 9.4 m thick, a carbonaceous mudstone layer approximately 0.2 m thick, a mudstone layer approximately 0.9 m thick, the 11-3 coal seam approximately 0.2 m thick, and another sandy mudstone layer approximately 10.7 m thick. During the trial, four boreholes were drilled, with boreholes No. 1 to No. 3 designated for data collection and borehole No. 4 used as a test borehole. The equipment used during the test is shown in Table 2.

During the construction of boreholes No. 1 to No. 3, under the unified control of the rig controller, the smart drilling rig and the CSTT system collaborated to collect drill cuttings samples every 0.75 m, and a total of 294 drill cuttings images were collected. The acquired images, as shown in Figure 14, are transmitted to the drilling rig control center for unified numbering and preprocessing. The numbering of the images includes three parts: image acquisition time, borehole number, and image sequence number.

Through observation of the collected cuttings samples, there are distinct visual differences between sandy mudstone-dominated rock cuttings and coal cuttings. The sandy mudstone wetted by drilling fluid exhibits a dark red color, which forms a sharp contrast with the bright black coal, providing favorable conditions for accurate computer classification. Moreover, by applying the method proposed in this study to avoid mixing cuttings from different strata, the lithology of the cuttings accurately reflects the borehole position. When the borehole remains within a single stratum, the cuttings display uniform lithological characteristics. As the borehole approaches the interface between two strata, the cuttings gradually show a distinct mixture of lithologies from both formations. Once the borehole crosses the interface and stabilizes within the new stratum, residual cuttings from the previous layer may still influence the sample. However, the amount of these mixed particles is minimal (<2% volumetric content) and does not significantly affect the overall lithological composition of the current cuttings.

Following the drill cuttings particle image preprocessing method, high-resolution raw images collected from Boreholes No. 1–3 were processed. Stratigraphic variations along the borehole trajectory were identified based on borehole survey and logging data, allowing drill cuttings images to be classified by lithology. Since this experiment focused on the construction of cross-measure boreholes mainly within sandstone and coal seams, there were insufficient samples of mudstone and carbonaceous mudstone. For simplicity, samples were categorized into two classes: coal and rock. Images of drill cuttings at coal-rock interfaces were removed to improve lithological labeling accuracy. Images were then cropped to meet the input size requirements of each pretrained model, yielding images of 224 × 224 pixels. This produced a training data set of 25 543 image blocks.

The training phase utilizes the previously mentioned data set as the training data set for all models using the Stochastic Gradient Descent with Momentum (SGDM) optimization algorithm. The initial learning rate is set to 0.01, with a mini-batch size of 128, maximum epochs of 30, and validation frequency of 50. Thirty percent of the training data set is used for model validation, while the remaining 70% is utilized for actual training. Figure 15 illustrates the training accuracy and loss trends for the three different models, showing rapid convergence within fewer than 60 iterations and stabilization at 100% accuracy and low loss values.

In this task, the NasNet-Mobile and Inception-v3 models rapidly achieved nearly 100% accuracy within the first 20 iterations, with the training loss quickly dropping to near zero, demonstrating fast and stable convergence. This performance is primarily due to their optimized architectures, efficient parameter settings, and well-initialized weights. In contrast, the GoogLeNet model showed significant fluctuations in accuracy and loss during the initial 20 iterations before gradually stabilizing and reaching high accuracy. This behavior is likely due to its more complex structure, requiring more time to adjust weights and parameters effectively. Overall, the differences in model architectures, initial parameter choices, learning rates, and data preprocessing methods are the main factors contributing to the varied training performances observed among these models.

3.2 Lithology identification generalization test results

To further test the model's accuracy and generalization ability, a new data set was sourced from borehole No. 4, which was drilled to a total depth of 81.75 m. Every 0.75 m, the CSTT system automatically captured one drill cuttings image, resulting in a collection of 109 images.

After cropping, numbering, and initial screening, 9811 usable test image samples were obtained. To accurately label the lithology of each test sample, this study utilized the YZD12 type borehole multiparameter visualization instrument. This instrument conducted video imaging of the borehole, providing precise information about coal-rock variations along the depth of the borehole, as shown in Table 3.

Using the preprocessing method previously described, the raw drill cuttings images were processed. The resulting images were categorized by lithology, resulting in 2028 images labeled as coal cuttings samples and 7783 images labeled as rock cuttings samples. Using three different models, the identification results for the test data set are depicted in the confusion matrix shown in Figure 16. Each model rapidly provides lithology identification results and accuracy for the drill cuttings image samples. Notably, the GoogLeNet network has the highest accuracy of the three single identification models in recognizing both coal and rock samples.

Coal-rock real-time recognition falls under the realm of pattern recognition and constitutes a binary classification task. Evaluation metrics employed include accuracy, precision, recall, and F1 score. Based on the correspondence between actual results and predicted results, the recognition accuracy of each model can be calculated, as shown in Table 4. The table also records the time taken by each model to complete the recognition task for all image samples. It is evident from the table that among the three models, GoogLeNet achieves the highest recognition speed and accuracy, with an average image recognition time of approximately 0.02 s per image.

To determine the impact of the weights of three classification models on the accuracy of the ensemble model, we analyzed the identification results of each model. Due to the inherent differences in identification results from different models for the test data set samples, as shown in Figure 17, the ensemble learning model achieved the highest recognition accuracy of 97.42% when the weights for GoogLeNet, Inception-v3, and NasNet-Mobile were set to 0.47, 0.31, and 0.22. The recognition time was longer compared to other models, taking approximately 0.11 s per image.

Figure 18 presents the computed results. In the figure, bar charts are used with different fill colors to represent the current identification results. Above each bar, a curve indicates the probability that the recognition result corresponds to the current value, ranging from 0.5 to 1.0.

From the results, each model successfully detected stratigraphic changes along the borehole trajectories. The ensemble learning method, which combines multiple trained models, produced identification results closest to the actual observations presented in Table 3. Since the drill cuttings were sampled every 0.75 m, while the data measured by the YZD12 borehole multiparameter visualization instrument was continuous, there was some discrepancy between the formation information detected by the CSTT system and the actual data. Significant variations in the proportion of coal and rock fragments were observed at coal-rock interfaces. This indicates that despite taking measures, the mixing of cuttings is difficult to avoid in the initial period after entering a new formation. As a result, the starting point of a coal seam should be identified by the appearance of coal fragments in the cuttings, while the transition to a new rock layer should be marked by the appearance of rock fragments in the coal cuttings.

The model exhibited fluctuations in recognition results during drilling at depths of 8.25–9.75 and 75.00–76.50 m. Investigation using drilling video footage revealed the presence of previously unidentified carbonaceous mudstone layers at these depths. The similarity in color and shape between carbonaceous mudstone fragments and coal fragments influenced the recognition results. The method of detecting stratigraphic changes using drill cuttings image samples exhibits a certain time delay. This delay is attributed to the time required for the transport of cuttings and increases with the depth of the borehole. These observations highlight the effectiveness of the models in identifying stratigraphic variations along borehole trajectories, the challenges posed by unexpected lithological features on model performance.

3.3 Real-time field application

In the construction of an additional seven boreholes within the same roadway, the lithology identification method and full equipment suite proposed in this study were applied. During drilling, the CSTT system automatically collected drill cuttings images every 0.75 m and fed them into the lithology identification model for stratigraphic recognition. The predicted lithology was subsequently compared and validated against re-survey results, achieving an overall model accuracy of 92.35%.

Based on these predictions, drilling parameters were adjusted upon detecting lithology changes. For rock seams, high rotational speeds (160–200 r/min) and relatively low thrust (40–55 kN) were employed to prevent excessive weight on bit (WOB) from causing drill string buckling, thereby enhancing drilling efficiency through increased cutting frequency. For coal seams, a higher thrust (900–110 kN) and lower rotational speeds (80–100 r/min) were used to minimize rotational oscillation effects on borehole wall stability, reducing collapse incidents while increasing penetration depth per rotation to maintain operational efficiency. Under these optimized parameters, no collapses occurred and the average rate of penetration (ROP) in these boreholes exceeded that of a neighboring borehole of the same depth by 21.32%.

It is acknowledged that the proposed model has certain limitations. Since the model was developed using data specific to the Huainan coalfield, its performance may vary in different geological formations. Thus, further research is needed to examine the model's generalizability across diverse geological environments.