Prediction Of Penetration Rate Research Articles

APPLICATION OF PSO-LSSVM IN PREDICTION AND ANALYSIS OF SLOW DRILLING (RATE OF PENETRATION)

The benefits of drilling include reducing the total time, maintaining the lowest possible risk, saving costs, and increasing efficiency, which occurs in (the planning and exploration stages). Slow drilling refers to a rate of penetration (ROP) that is not at the desired level. ROP characterizes the speed at which the drill bit penetrates the underlying rock to deepen the borehole, as it is directly related to controlling the speed and efficiency of drilling which ultimately impacts development costs. Predicting ROP is a very important step to optimize drilling with Machine Learning that can assist in solving complex problems with maximum possible efficiency. The model used is PSO-LSSVM treats the penetration drill bit as a continuous process. It takes drilling data sequentially, continuously predicts ROP, and achieves better ROP prediction results. In this case, Hole Depth, weight on bit (WOB), Bit Rotation per minute (RPM), Torque, Bit Depth, Time of Penetration, Hook Load, and Standpipe Pressure, demonstrated influence in keeping ROP at a high level. According to the results, the PSO-LSSVM algorithm can be used for the prediction of ROP in well X. thus providing a solution for prediction and control of operating effects which can result in a fast penetration rate and better efficiency in drilling.

Research Progress on Prediction of Penetration Rate of Oil Drilling Machinery

Research Progress on Prediction of Penetration Rate of Oil Drilling Machinery

A novel Hybrid XGBoost Methodology in Predicting Penetration Rate of Rotary Based on Rock-Mass and Material Properties

Predicting the drill penetration rate is a fundamental requirement in mining operations, profoundly impacting both the cost-effectiveness of mining activities and strategic mine planning. Given the intricate web of factors influencing rotary drilling performance, the necessity for advanced modeling techniques becomes evident. To this end, the hybrid extreme gradient boosting (XGBoost) was utilized to gauge the penetration rate of rotary drilling machines, utilizing random search, grid search, Harris Hawk optimization (HHO), and the dragonfly algorithm (DA) as metaheuristic algorithms. Our research draws from extensive data collected in copper mine case studies, encompassing both field and investigational data. This dataset incorporates critical material properties, such as tensile strength (TS), uniaxial compressive strength (UCS), as well as vital rock-mass characteristics including joint direction (JD), joint spacing (JS), and bit diameter (D). Our investigation evaluates the reliability of these prediction methods using various performance indicators, including mean absolute error (MAE), root mean square error (RMSE), average absolute relative error (AARE), and coefficient of determination (R2). The multivariate analysis reveals that the HHO-XGB model stands out, demonstrating superior prediction accuracy (MAE: 0.457; RMSE: 2.19; AARE: 2.29; R2: 0.993) compared to alternative models. Furthermore, our sensitivity analysis emphasizes the substantial impact of uniaxial compressive strength and tensile strength on the drill penetration rate. This underlines the importance of considering these material properties in mining operations. In conclusion, our research offers robust models for forecasting the penetration rate of similar rock formations, providing invaluable insights that can significantly enhance mining operations and planning processes.

Drilling Parameters Multi-Objective Optimization Method Based on PSO-Bi-LSTM

The increasing exploration and development of complex oil and gas fields pose challenges to drilling efficiency and safety due to the presence of formations with varying hardness, abrasiveness, and rigidity. Consequently, there is a growing demand for drilling parameter optimization and speed-up technologies. However, existing models based on expert experience can only achieve single-objective optimization with limited accuracy, making real-time adaptation to changing drilling conditions and formation environments challenging. The emergence of artificial intelligence provides a new approach for optimizing drilling parameters. In this study, we introduce the Bi-directional Long Short-Term Memory (Bi-LSTM) deep learning algorithm with the attention mechanism to predict the rate of penetration (ROP). This algorithm improves the ROP prediction accuracy to 98.33%, ensuring reliable subsequent optimization results. Additionally, we propose a coupling optimization algorithm that combines Bi-LSTM with the particle swarm optimization algorithm (PSO) to enhance drilling efficiency through parameter optimization. Our approach aims to maximize drilling footage while maintaining the highest ROP. The optimal solutions obtained are verified through multi-parameter cloud image analysis, yielding consistent results. The application of our approach demonstrates an 81% increase in drilling speed and a 28% reduction in drill bit energy losses. Moreover, the real-time optimization results effectively guide field operations.

A Sequential Feature-Based Rate of Penetration Representation Prediction Method by Attention Long Short-Term Memory Network

Summary In the petroleum and gas industry, optimizing cost-effectiveness remains a paramount objective. One of the key challenges is enhancing predictive models for the rate of penetration (ROP), which are intricately tied to the delicate interplay between significant parameters and drilling efficiency. Recent research has hinted at the potential of temporal and sequential elements in drilling, but a detailed exploration and understanding of these dynamics remain underdeveloped. Addressing this research gap, our primary innovation is not just the introduction of a model but rather the employment of the attention-based long short-term memory (LSTM) network as a tool to deeply analyze the role of sequential features in ROP prediction. Beyond merely applying the model, we furnish a robust foundation for sequential analysis, detailing data processing methods and laying out comprehensive data analytics guidelines for such temporal assessments. The utilization of the LSTM network, in this context, ensures meticulous capture of real-time drilling data nuances, providing insights that are both profound and actionable. Through empirical evaluations with real-world data sets, we accentuate the vital importance of time-sequential dynamics in refining ROP predictions. Our methodological approach, tailored for the oilfield domain, is both rigorous and illuminating, achieving an R2 score of 0.95 and maintaining a relative error under 10%. This effort goes beyond simply proposing a new predictive mechanism. It establishes the centrality of sequential analysis in the drilling process, charting a course for future research and operational optimization in the petroleum and gas sector. We not only offer enhanced modeling strategies but also pioneer insights that can shape the next frontier of industry advancements.

Data Knowledge Dual-Driven Rate of Penetration Prediction Method for Horizontal Wells

The prediction and optimization of the rate of penetration (ROP) for horizontal wells are more complicated than for vertical wells, but most of the current ROP prediction studies are for vertical wells, which cannot be adapted to the complex drilling characteristics of horizontal wells. To this end, this paper proposes a data knowledge dual-driven horizontal well ROP prediction method. Firstly, the drilling characteristics of horizontal wells are analyzed, showing that the horizontal wells ROP prediction model cannot be modeled using surface measurement data; secondly, based on the analysis of horizontal well drilling characteristics, a physical model-based horizontal well ROP modeling data pre-processing method is proposed by introducing the drag and torque model. Finally, a data knowledge dual-driven horizontal well ROP prediction method is proposed in conjunction with data-driven algorithms. The proposed horizontal well ROP prediction method is applied to the A1~A4 wells in the Sichuan area. Compared with the conventional data-driven ROP prediction method, the prediction accuracy of this method is improved by 30%. The proposed method can provide a basis for the intelligent optimization and management of ROP during the drilling of horizontal wells.

Interpretation and characterization of rate of penetration intelligent prediction model

Accurate prediction of the rate of penetration (ROP) is significant for drilling optimization. While the intelligent ROP prediction model based on fully connected neural networks (FNN) outperforms traditional ROP equations and machine learning algorithms, its lack of interpretability undermines its credibility. This study proposes a novel interpretation and characterization method for the FNN ROP prediction model using the Rectified Linear Unit (ReLU) activation function. By leveraging the derivative of the ReLU function, the FNN function calculation process is transformed into vector operations. The FNN model is linearly characterized through further simplification, enabling its interpretation and analysis. The proposed method is applied in ROP prediction scenarios using drilling data from three vertical wells in the Tarim Oilfield. The results demonstrate that the FNN ROP prediction model with ReLU as the activation function performs exceptionally well. The relative activation frequency curve of hidden layer neurons aids in analyzing the overfitting of the FNN ROP model and determining drilling data similarity. In the well sections with similar drilling data, averaging the weight parameters enables linear characterization of the FNN ROP prediction model, leading to the establishment of a corresponding linear representation equation. Furthermore, the quantitative analysis of each feature's influence on ROP facilitates the proposal of drilling parameter optimization schemes for the current well section. The established linear characterization equation exhibits high precision, strong stability, and adaptability through the application and validation across multiple well sections.

Intelligent Prediction of Drilling Rate of Penetration Based on Method-Data Dual Validity Analysis

Summary The rate of penetration (ROP) is a critical parameter in drilling operations, essential for optimizing the drilling process and enhancing drilling speed and efficiency. Traditional and statistical models are inadequate for predicting ROP in complex formations, as they fail to conduct a comprehensive analysis of method validity and data validity. In this study, geological conditions parameters, mechanical parameters, and drilling fluid parameters were extracted as prediction parameters, and an intelligent ROP prediction method was constructed under method-data dual validity analysis. The effectiveness of the ROP prediction method is studied by comparing five machine learning algorithms. The data validity of ROP prediction is also studied by changing the input data type, input data dimension, and input data sampling method. The results show that the effectiveness of the long short-term memory (LSTM) neural network method was found to be superior to support vector regression (SVR), backpropagation (BP) neural network, deep belief neural network (DBN), and convolutional neural network (CNN) methods. For data validity, the best input data type for ROP prediction is geological conditions parameters after principal component analysis (PCA) combined with mechanical parameters and drilling fluid parameters. The lower limit of input data dimension validity is seven input parameters, and the accuracy of prediction results increases with the increase of data dimension. The optimal data sampling method is one point per meter, and the error of the prediction result increases and then decreases with the increase of sampling points. Through step-by-step analysis of method validity, input data type, input data dimension, and input data sampling method, the range, size, and mean of error values of ROP prediction results were significantly reduced, and the mean absolute percentage error (MAPE) of the prediction results of the test set is only 18.40%, while the MAPE of the prediction results of the case study is only 11.60%. The results of this study can help to accurately predict ROP, achieve drilling speedup in complex formations, and promote the efficient development of hydrocarbons in the study area.

Predicting the Market Penetration Rate of China’s Electric Vehicles Based on a Grey Buffer Operator Approach

On the decision of whether to continue to implement the industrial support policy, two scenarios are set to predict the market penetration rate of China’s electric vehicles (EVs) (In this paper, the term Electric Vehicles (EVs) refers to both full-battery EVs and plug-in hybrids). In order to weaken the disturbance caused by international oil prices and industrial policies, the grey buffer operator was firstly applied, to preprocess the original data series. The sales data for EVs and fuel vehicles were buffered for second order and first order, respectively. Based on the obtained buffer data sequence, the GM (1, 1) model was used to predict the sales of EVs and fuel vehicles between 2022 and 2025 in China. The results demonstrate a significantly improved fit compared to directly modeling the raw data. This method is suitable for studying the market penetration rate prediction of China’s EVs. If the industry support policies continue (Scenario I), an EV market penetration rate of 22.45% can be achieved in 2024, and the expected target can be achieved one year ahead of schedule. Even if the corresponding industrial support policies are no longer implemented (Scenario II), the EV market penetration rate will reach 20.58% in 2025, and the set target of 20% will be achieved on schedule.

Real-time rate of penetration prediction for motorized bottom hole assembly using machine learning methods

Drilling rate of penetration (ROP) is one of the most important factors that have their significant effect on the drilling operation economically and efficiently. Motorized bottom hole assembly (BHA) has different applications that are not limited to achieve the required directional work but also it could be used for drilling optimization to enhance the ROP and mitigate the downhole vibration. Previous work has been done to predict ROP for rotary BHA and for rotary steerable system BHA; however, limited studies considered to predict the ROP for motorized BHA. In the present study, two artificial intelligence techniques were applied including artificial neural network and adaptive neurofuzzy inference system for ROP prediction for motorized assembly in the rotary mode based on surface drilling parameters, motor downhole output parameters besides mud parameters. This new robust model was trained and tested to accurately predict the ROP with more than 5800 data set with a 70/30 data ratio for training and testing respectively. The accuracy of developed models was evaluated in terms of average absolute percentage error, root mean square error, and correlation coefficient (R). The obtained results confirmed that both models were capable of predicting the motorized BHA ROP on Real-time. Based on the proposed model, the drilling parameters could be optimized to achieve maximum motorized BHA ROP. Achieving maximum ROP will help to reduce the overall drilling cost and as well minimize the open hole exposure time. The proposed model could be considered as a robust tool for evaluating the motorized BHA performance against the different BHA driving mechanisms which have their well-established models.

Establishment of data-driven multi-objective model to optimize drilling performance

Drilling parameters optimization has consistently generated research interest over the years because of the cost-saving benefits associated to improve drilling efficiency. However, several physics-based and data-driven models have been developed for drilling parameters optimization, and the majority of the data-driven models are based on regression methods. Obtaining highly accurate and optimized drilling parameters with rapid as well as cost-effective simulation runs is difficult to achieve. To accurately and rapidly predict drilling parameters, a multi-objective optimization model was proposed in this study. In the proposed model, the rate of penetration (ROP), unit drilling cost (UDC), and mechanical specific energy (MSE) were considered as the objective functions, while the weight on bit (WOB) and rotations per minute (RPM) were chosen as the optimization variables. Meanwhile, a method for ROP prediction based on improved back propagation neural network (BP) is also presented. Field data presented in this study indicate that when drilling is free of drilling complications, this multi-objective optimization model could optimize WOB and RPM with higher ROP and lower MSE, and UDC.

An explainable ensemble machine learning model to elucidate the influential drilling parameters based on rate of penetration prediction

Many researchers have examined the benefits of machine learning (ML) algorithms in geothermal drilling, especially for predicting the rate of penetration (ROP) of drilling. However, little attention has been given to enhancing the explainability of ML algorithms. Motivated by this, this study examines the feasibility of an explainable ML approach for elucidating the behavior of the drilling parameters to generate explainable insights regarding the surface-controllable drilling parameters that could maximize the ROP for drilling effectiveness (DE). First, this study presents a performance analysis using hyper-parameter technique of the ensemble supervised machine learning (ESML) for ROP predictions. Secondly, this study uses an explainable ML method to explain the drilling parameters' behavior based on the ESML prediction. The results show that the ensemble CatBoost model shows the best performance for ROP prediction. Then, the explainable ML successfully revealed several influential drilling parameters that could maximize the ROP for DE. In earlier drilling (0–4,000 m in depth), temperature and pressure contribute to a lower ROP, and the drilling fluid flow rate and rotary speed contribute to a higher ROP. On the other hand, as the drills go deeper into the earth (4,000–7,536 m in depth), maximum ROP can be obtained by maximizing the drilling pressure, weight on bit (WOB), surface torque, and rotary speed (RPM) of the drill bit, while drilling fluid flow rate does not significantly contribute to ROP. The explainable ML also highlighted the drilling parameters' inflection points when a switch from low to high rates of ROP occurs. Overall, the findings show the proposed model's strength in generating explainable insights that can improve the selection of appropriate ROP settings and support future decision-making processes in geothermal drilling.

Multi-source information fusion-based dynamic model for online prediction of rate of penetration (ROP) in drilling process

Rate of penetration (ROP) accurate prediction can effectively reduce drilling cycle time and operation cost. However, the complexity and variability of drilling process is not conducive to accurate ROP modeling. The existing ROP models focus on static prediction considering drilling data, which cannot effectively solve the ROP mutation problem caused by complex formations. Some ROP online prediction studies have great real-time capture capability, but without considering the formation lithology adequately, and can be limited in practical application. In this paper, a multi-source information fusion-based dynamic model for online prediction of ROP in drilling process is proposed, which consists of three stages (drilling data pre-processing, optimized ROP modeling, and ROP model updating). In the first stage, three drilling parameters including weight on bit, rotational speed, and torque are filtered by using the outlier removing and wavelet filtering techniques. In the second stage, a novel ROP modeling method, named as hybrid bat algorithm optimized - restricted Boltzmann machine - back propagation neural network (HBAO-RBM-BPNN) is proposed to build the ROP prediction model. In the last stage, formation drillability variation and time interval are used as update conditions, and the established ROP model is updated via a moving window strategy to realize high accuracy online prediction of ROP. Multi-source (formation and drilling) information fusion is considered in this key stage. Finally, the proposed method and seven ROP prediction methods (two offline and five online) are compared through the use of industrial data from a drilling site in Dandong area, Northeast China. The comparison results verify the effectiveness of the proposed method, which lays a foundation for intelligent optimization control of complex geological drilling process.

Interpretable Feature Construction and Incremental Update Fine-Tuning Strategy for Prediction of Rate of Penetration

Prediction of the rate of penetration (ROP) is integral to drilling optimization. Many scholars have established intelligent prediction models of the ROP. However, these models face challenges in adapting to different formation properties across well sections or regions, limiting their applicability. In this paper, we explore a novel prediction framework combining feature construction and incremental updating. The framework fine-tunes the model using a pre-trained ROP representation. Our method adopts genetic programming to construct interpretable features, which fuse bit properties with engineering and hydraulic parameters. The model is incrementally updated with constant data streams, enabling it to learn the static and dynamic data. We conduct ablation experiments to analyze the impact of interpretable features’ construction and incremental updating. The results on field drilling datasets demonstrate that the proposed model achieves robustness against forgetting while maintaining high accuracy in ROP prediction. The model effectively extracts information from data streams and constructs interpretable representational features, which influence the current ROP, with a mean absolute percentage error of 7.5% on the new dataset, 40% lower than the static-trained model. This work provides a theoretical reference for the interpretability and transferability of ROP intelligent prediction models.

Prediction of the rate of penetration in offshore large-scale cluster extended reach wells drilling based on machine learning and big-data techniques

Large-scale cluster extended reach wells (ERWs) are usually drilled from one platform to target distant multiple reservoirs in offshore oil & gas development. Due to the complicated downhole environments and complex geological conditions in various azimuths, the accurate prediction of the rate of penetration (ROP) in offshore large-scale cluster ERWs drilling is a challenging task. In this work, a ROP prediction method for offshore large-scale cluster ERWs drilling based on machine learning and big-data techniques is presented. 27 input parameters affecting ROP are collected from block N of China Bohai Oilfield, with a total of 9,193,408 data points. The internal relation between various parameters and ROP at the data level is revealed, which verifies the feasibility of the collected data set training. Based on various machine learning algorithms, 12 kinds of ROP prediction models based on support vector regression (SVR), random forest regression (RFR), back propagation neural network (BPNN), and recurrent neural network (RNN) are established respectively. Meanwhile, the prediction accuracy of the ROP prediction models is evaluated based on six performance indicators. It shows that the prediction results of the RNN model with LSTM as neuron structure are the best (all of the 6 indicators are optimal) among the 12 kinds of ROP prediction models, and the mean absolute error is only 6.12 m/h. The RNN model with LSTM as neuron structure is the best machine learning model in the prediction of ROP in offshore large-scale cluster ERWs drilling. It provides a practical method for enhancing cleaner development of offshore oil & gas.

Artificial intelligence for tunnel boring machine penetration rate prediction

Penetration rate prediction of Tunnel Boring Machines (TBM) is critical for understanding excavation performances. In this paper, we investigate the possibility of developing machine learning models that accurately predict the Penetration Rate of a TBM using only machine parameters. We leveraged two datasets collected from the Exploratory and Main excavation of the Lot Mules 2–3 of the Brenner Base Tunnel Project. We compared the performance of two different Artificial Neural Network architectures, one based on feedforward architecture and the other on long short-term memory (LSTM). We also studied which features lead to a good estimation of the penetration rate using SHAP, an explainable AI tool, discovering that the Specific Energy (SE) and the Cutterhead Power (CP) are the most impactful features. We also explore the possibility of performing cross-tunnel prediction by training the model on the Exploratory tunnel and testing it on the Main Tunnel, obtaining promising results.

Rockmass Characteristics and its Influence on TBM Penetration Rate in Archeans and Meta-sedimentary Rocks of India

TBM penetration rate prediction models were developed for the hard Archean granites, granite gneisses and Meta-sedimentary rock groups comprising of quartzites, quartz arenites and sandstone of peninsular India.Construction stage engineering geological mapping comprising of face logging resulted in rockmass characterisation of Archeans and Meta-sedimentary rock groups. Statistical correlation of TBM penetration rate (PR) with Rock quality designation (RQD), unconfined compressive strength (UCS), volumetric joint count (Jv) and rockmass class was carried out. The prediction model provides good correlation only in the Meta sedimentary rock group for RQD, UCS and rockmass class and good correlation only for RQD and rockmass class in the Archean group but poor correlation with UCS. There is an exponential increase of penetration rate with Jv in both the rock groups. Keywords: TBM Penetration Rate, Rockmass Characteristics, Geological Mapping, Rockmass Classification, Rock Tunnel

Wide and deep cross network for the rate of penetration prediction

Rate of penetration (ROP) prediction is of great significance for drilling optimization. However, most existing intelligent ROP prediction models are entirely data-driven, and the mobility and stability of the model still need to be improved. Through the analysis of the modified Young's ROP Equation, it was observed that there are linear, nonlinear, and cross multiplication relationships between ROP and multiple drilling parameters, making it difficult for conventional deep neural networks (DNN) to fully explore the complex correlation information between various parameters and ROP. Based on the Wide and Deep Learning (WDL) model and Deep and Cross neural network (DCN) model, an innovative Wide and Deep Cross neural network (WDCN) was proposed in this research to fit the complex correlation between multiple drilling parameters and ROP. And the ROP prediction model based on Bourgoyne avdnd Young Equation, DNN, WDL, DCN, and WDCN were constructed, trained, and tested using drilling data from 25 wells in the Tarim Oilfield. Using the test results of four cases, the mean absolute error, mean relative error, and R2 of different ROP prediction models were compared and analyzed. The results indicate that the WDCN ROP prediction model performs best in the same and similar formations of two adjacent wells compared to other ROP prediction models. The WDCN model's MRE reduces the least, demonstrating high fitting ability and migration capabilities.

A Survey on the Shortcomings of the Current Rate of Penetration Predictive Models in Petroleum Engineering

In drilling engineering, the rate of penetration (ROP) is a prevalent indicator to evaluate the energy efficiency of drilling operations. Nowadays, ROP prediction has become more critical since the production from deeper hydrocarbon resources is unprecedentedly escalating. So far, a wealth of theoretical and practical investigations has been conducted to develop ROP models; however, the existing models have not been adequately updated with the new technological advancements or geological restrictions. This research strives to integrate the latest advancements, restrictions, and future requirements in ROP prediction. To do this, the existing empirical and data-driven ROP models are elaborated and compared. From the conducted research, it was deduced that four uncontrollable factors, including the rock permeability, wellbore inclination, temperature, and rock hardness, have not been adequately considered in ROP models. Moreover, although data-driven ROP models deliver more accurate results than the empirical models, the determination of the number and type of the input parameters is still challenging. To tackle this issue, it is recommended to develop a formation-based classification system of input parameters for future investigations. This inclusive review can be adopted by the companies and engineers involved in drilling operations to update and reform their drilling strategies.

Real-time prediction of multivariate ROP (rate of penetration) based on machine learning regression algorithms: Algorithm comparison, model evaluation and parameter analysis

ROP (Rate of Penetration) is a comprehensive indicator of the rock drilling process and how efficiently predicting drilling rates is important to optimize resource allocation, reduce drilling costs and manage drilling hazards. However, the traditional model is difficult to consider the multiple factors, which makes the prediction accuracy difficult to meet the real drilling requirements. In order to provide efficient, accurate and comprehensive information for drilling operation decision-making, this study evaluated the applicability of four typical regression algorithms based on machine learning for predicting pore pressure in Troll West field, namely SVR (Support Vector Regression), Linear regression, Regression Tree and Gradient Boosting regression. These methods allow more parameters input. By comparing the prediction results of these typical regression algorithms based on R2(R-Square), explained variance, mean absolute error, mean squared error, median absolute error and other performance indicators, it was found that each method predicted different results, among which Gradient Boosting regression has the best results, their prediction accuracy is high and the error is very low. The prediction accuracy of these methods is positively correlated with the proportion of the training data set. With the increase of logging features, the prediction accuracy is gradually improved. In the prediction of adjacent wells, the ROP prediction methods can achieve a certain prediction effect, which shows that this method is suitable for ROP prediction in Troll West field.