Keywords

1 Introduction

With the rapid development of modern artificial intelligence technology, the recognition of oil drilling status has become a hot topic of research. To ensure the safety and efficiency of the drilling process, this paper aims to explore the use of artificial intelligence algorithms, such as machine learning and deep learning methods, to provide new solutions for the recognition of oil drilling status. Despite many recent studies attempting to use artificial intelligence technologies like decision trees [1], support vector machines [2], and deep learning for condition recognition, these studies have certain limitations in evaluation methods and applications. This paper aims to address these limitations, proposing more scientific evaluation criteria and building a unified drilling status dataset.

Firstly, this paper will introduce a new drilling status dataset, which covers various types of status information, providing a solid foundation for the application of artificial intelligence algorithms. Drawing on the classic ImageNet [3] dataset in the field of computer vision, we hope to provide a shared, standardized data foundation for researchers in the field of oil drilling status recognition through the construction of this dataset, thereby promoting algorithm innovation and development.

Secondly, to address the limitations of existing research in evaluation methods, this paper will use a more scientific evaluation standard, namely the F1 score [4]. The F1 score combines precision and recall, enabling a more comprehensive evaluation of model performance. Comparing various algorithms on a unified drilling status dataset, we will be able to gain a deeper understanding of the strengths and weaknesses of various methods, providing more valuable guidance and insights for the field of oil drilling status recognition.

Based on a unified dataset and evaluation standards, this paper will comprehensively assess and improve existing oil drilling status recognition methods. After conducting a detailed empirical analysis of various algorithms, this paper will propose an optimized algorithm for oil drilling status recognition, aiming to achieve high-precision condition prediction, thus enhancing the safety of the drilling process.

In conclusion, the work of this paper will provide a powerful inspiration and guidance for the future development of oil drilling status recognition research. We believe that with the continuous innovation and application of artificial intelligence technology, the field of oil drilling will welcome a safer and more efficient development. By analyzing the characteristics of various conditions and designing corresponding artificial intelligence algorithms based on these characteristics, this paper will help to further advance the research on oil drilling status recognition, providing more effective and reliable solutions for future practical applications.

2 Drilling Status Dataset

2.1 Collection of Drilling Status

The dataset is collected from real logging data in the Engineering Intelligent Support Center (EISC) system, downloaded through the EISC data lake, and provided by the Xinjiang Oilfield Engineering Institute and the Junggar Project Department in three ways: collecting design documents, logs, well histories, logging, well testing and other data. Data from 23 completed wells in different regions was collected, and the DDR drilling status intelligent recognition system was used to complete the structuring and standardization of the logs; the DDR accident complexity intelligent recognition system was used to identify and construct a complex accident ledger. The data were categorized according to 9 normal drilling statuses including drilling, circulation, reaming, and casing, and calibrated by specialized experts. A total of 224,781 status data records were processed, with 153,776 valid data records (excluding status data with empty features in any field). To ensure the accuracy of the status labels, the data annotation was performed by five field experts, and the final drilling status label was determined by majority vote. This dataset can be used by researchers to study and develop intelligent engineering operation support systems.

2.2 Analysis of Drilling Status Dataset

This drilling dataset includes nine drilling statuses: composite drilling, casing running, back reaming, directional drilling, drilling down, circulation, single joint connection, pulling out of hole, and. The specific statistics for each drilling status are shown in Fig. 1: among them, composite drilling is the most common, and reaming data is relatively less.

Fig. 1.
figure 1

Statistics of different drilling status in the drilling data set

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In all drilling status data, there are 17 key features (units in parentheses): torque (kN·m), total pit volume (m3), weight on bit (kN), inlet flow rate (L/s), rotary table speed (rpm), outlet flow rate (L/s), delayed drilling depth (m), standpipe pressure (MPa), well depth (m), number one pump stroke (spm), drill bit position (m), outlet flow rate percentage (%), number two pump stroke (spm), number three pump stroke (spm), hook load (kN), hook height (m), and casing pressure (MPa). These features, which originate from historical manual drilling status judgments, are of crucial value for drilling status recognition. This study will use these features to apply artificial intelligence algorithms to predict drilling status categories, aiming to improve recognition accuracy and practicality.

3 Drilling Status Recognition Tasks and Experiments

In the previous section, we introduced the drilling status dataset. In this section, we introduce the task of drilling status recognition into the field of machine learning, thus achieving more efficient and accurate recognition. For this task, we will outline the basic processes involved and describe the core steps of machine learning in handling classification tasks. This paper will focus on using the proposed dataset to evaluate different machine learning algorithms, analyze, and propose future research directions.

3.1 Overview of Machine Learning Algorithm Development

Drilling status recognition, as a classification task, aims to use machine learning algorithms to automatically recognize different drilling statuses. Based on this goal, we can divide the entire processing flow into the following key steps, as shown in Fig. 2:

  1. 1.

    Data Preparation: First, the drilling status dataset needs to be preprocessed, including data cleaning, handling missing values, outlier processing, and feature engineering, etc., to ensure data quality and usability.

  2. 2.

    Feature Selection: After data preprocessing, it is necessary to determine the most representative and discriminative features for the specific classification task through feature selection techniques to improve the performance of the classification model.

  3. 3.

    Model Selection and Training: Next, according to task requirements and data characteristics, select an appropriate machine learning algorithm, and use the training dataset to train the model to learn the association between features and drilling status categories.

  4. 4.

    Model Evaluation and Optimization: After model training, predict the test dataset to evaluate the model’s performance. If the evaluation results are unsatisfactory, the model can be adjusted and optimized to improve classification accuracy.

  5. 5.

    Application and Deployment: After the above steps, when a classification model that meets the requirements is obtained, it can be deployed in actual drilling scenarios to achieve automatic recognition and monitoring of drilling statuses.

Fig. 2.
figure 2

Diagram of machine learning algorithm development for drilling status recognition

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Based on the above process, this paper summarizes and organizes the drilling status recognition task. In this task, our goal is to predict the corresponding drilling status category based on the input drilling status data. Specifically, the input data includes a series of key features during the drilling process, such as torque, total pit volume, weight on bit, etc. The model generates the corresponding drilling status category as output by analyzing these features.

3.2 Drilling Status Recognition Algorithm

The purpose of this paper is to explore the performance of different machine learning algorithms in the task of drilling status recognition. Therefore, this study will select various commonly used multi-classification machine learning algorithms for experimentation, including: Logistic Regression (LR) [5], Support Vector Machine (SVM) [6], K-Nearest Neighbors (KNN) [7], Decision Tree Classifier (DTree) [8], Random Forest Classifier (RTree) [9], Multilayer Perceptron (MLP) [9], Gaussian Naive Bayes (GauNB) [9], AdaBoost Classifier (AdaB) [10], Gradient Boosting Classifier (GradB) [11].

In order to further improve the performance of status prediction, this paper introduces ensemble learning methods [12]. Ensemble learning is a strategy of combining multiple weak classifiers to form a strong classifier. In this research, we select models that have performed well in previous experiments as sub-models and construct a Voting Classifier to implement ensemble learning. The core idea of ensemble learning is to make the final prediction more stable and reliable by synthesizing the prediction results of multiple models. Ensemble learning has the following advantages: it reduces the risk of overfitting; improves prediction accuracy; enhances model stability; and handles diverse data.

In this paper, we construct a voting classifier by integrating multiple well-performing sub-models into a powerful classifier. This method is expected to improve the predictive performance of the drilling status recognition task, providing more reliable status recognition results for practical applications.

First, we need to preprocess the data. As the units among different features in the status data are different, we need to normalize the data before training. After normalization, the data will be in a unified scale range, which will help to improve the training effect and performance of the model.

Next, we will use the processed data to train various algorithms and evaluate the training results. In past research, the evaluation indicator usually used was accuracy. However, accuracy does not fully reflect the performance of the model in classification tasks. Therefore, this paper introduces the F1 score as the evaluation criterion.

The F1 score is the harmonic mean of Precision and Recall. Compared with accuracy, the F1 score has the following advantages: first, the F1 score takes into account both the precision and recall of the model, which makes the model have better evaluation effect when dealing with imbalanced datasets; second, the F1 score can calculate evaluation indicators for each category separately in multi-category classification problems, and then give an overall performance evaluation. In summary, the F1 score is a more comprehensive and robust evaluation indicator, which helps to understand the performance of different machine learning algorithms in the drilling status recognition task.

3.3 Experiment Analysis

Table 1. Prediction results for different machine learning algorithms
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Which Algorithm Performs Better?

In the experiments of different machine learning algorithms in the drilling status recognition task, we obtained the F1 scores and accuracy results as shown in Table 1. The analysis is as follows:

Random Forest and Gradient Boosting Classifier performed outstandingly in this experiment, with high F1 scores and accuracy, indicating that these two algorithms have strong predictive ability when dealing with the drilling status recognition task. The performance of Decision Tree is also relatively good, with high F1 scores and accuracy, which can be used as an alternative plan for further optimization and adjustment. Logistic Regression, Support Vector Machine, and K-Nearest Neighbors algorithms performed moderately. Although they may not meet the prediction requirements in this experiment, they may still have certain application value in specific scenarios. The performance of Multilayer Perceptron is close to K-Nearest Neighbors, but slightly inferior to Decision Tree, Random Forest, and Gradient Boosting Classifier. In practical applications, you can try to adjust its parameters to improve prediction performance. Gaussian Naive Bayes and AdaBoost classifiers performed poorly in this experiment, with low F1 scores and accuracy. In the drilling status recognition task, these two algorithms may not be the best choices.

Is Ensemble Learning Useful?

The Voting Classifier, as a method of ensemble learning, performed well in the experiment, with both F1 scores and accuracy reaching 98.1%. Although in this experiment, the performance of the Voting Classifier was slightly lower than that of the Random Forest and Gradient Boosting Classifier, it still demonstrated significant predictive capability.

The advantage of ensemble learning methods is that they integrate the prediction results of multiple sub-models, reducing the risk of overfitting of a single model, thereby enhancing the generalization ability of the model. The predictive performance of the Voting Classifier is influenced by the performance of multiple sub-models, so in practical applications, attempts can be made to optimize and adjust the sub-models to further enhance the predictive capability of the Voting Classifier.

In summary, although the performance of the Voting Classifier in this experiment was slightly lower than that of the Random Forest and Gradient Boosting Classifier, as an ensemble learning method, it still demonstrated high predictive performance. Future research could consider further optimization and adjustment of the sub-models of the Voting Classifier, to further improve the accuracy and practicality of drilling status recognition.

Does Different Features Have Different Impacts on the Model?

Indeed, feature selection has a significant impact on the performance of the model. By selecting appropriate features, the complexity of the model can be reduced, computational costs can be minimized, and prediction accuracy can be improved. Therefore, feature selection can be an important direction for future research. This article mainly presents a dataset suitable for various condition predictions, proposes a condition prediction task, and tests the performance of commonly used machine learning algorithms on the proposed dataset and tasks, so no special operations for feature selection were conducted. Subsequent research can explore how to select features that are more suitable for predicting all conditions. Furthermore, for different machine learning models, researchers can also try to carry out targeted feature selection to maximize the advantages of each model and further improve prediction performance.

In summary, feature selection is of significant importance in the task of drilling status recognition. Future research can explore from multiple perspectives how to choose more representative features to improve the predictive performance and practicality of the model.

4 Future Direction

While the prediction accuracy has already reached about 99%, there are still some research directions worth exploring in the field of drilling condition recognition:

  1. 1.

    Feature engineering: Although the existing features have achieved good prediction results, the feature set can still be optimized to enhance the model’s generalizability through further feature engineering, such as feature selection, dimensionality reduction, and feature construction.

  2. 2.

    Model fusion: Try to fuse different types of models, such as stacking, Bagging, and Boosting methods, to enhance the model’s stability and generalization performance.

  3. 3.

    Online learning and incremental learning: Drilling condition data may change over time. Researching online and incremental learning methods can enable the model to continuously update and optimize on new data, improving prediction capabilities.

  4. 4.

    Anomaly detection and handling: Anomalies may occur during the drilling process, and these anomalies may affect the prediction performance of the model. Researching anomaly detection and handling methods can enhance the model’s robustness when facing abnormal data.

  5. 5.

    Interpretability research: Improve the interpretability of the model, helping engineers understand the reasons for the model’s predictions, thus providing more targeted suggestions for drilling operations.

By exploring these research directions, the field of drilling condition recognition will continue to develop in the future, providing higher quality prediction and decision support for the drilling industry.

5 Conclusion

This paper introduces a brand-new drilling condition dataset and standardizes the task of condition recognition prediction. Based on the proposed dataset and tasks, we evaluated a variety of different machine learning algorithms and conducted a detailed analysis of the prediction performance of each algorithm. This research result provides a benchmark for subsequent researchers to facilitate more in-depth discussions in the field of condition recognition.

Through experiments and analysis of different machine learning algorithms, we revealed the strengths and weaknesses of each algorithm in the task of drilling condition recognition. In addition, we introduced ensemble learning methods and improved prediction performance by combining multiple excellent sub-models into a voting classifier.

This research not only provides a new data foundation and prediction standard for the task of drilling condition recognition but also provides useful insights for researchers in related fields. Future research can continue to explore more advanced machine learning algorithms and optimization techniques based on this paper, thus achieving more significant results in the field of drilling condition recognition. We hope this research can provide strong support for actual drilling operations and contribute to improving drilling efficiency and safety.