Drilling Operations Research Articles

Real-Time Monitoring and Control System Enhances Drilling-Fluid Management

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 216322, “Next-Generation Drilling-Fluid Management: Real-Time Monitoring and Autonomous Control,” by Zhicai Zhang, Rached M. Rached, SPE, and Yunbo Xun, Sinopec, et al. The paper has not been peer reviewed. _ Accurate measurement and control of drilling-fluid properties are crucial for safe and successful drilling operations. Traditional manual methods are slow and require significant manpower, underscoring the need for real-time monitoring. This paper highlights a new online system for monitoring drilling fluids, enabling intelligent control of drilling-fluid performance. The online monitoring system improves drilling-industry safety and success by measuring properties of drilling fluids in an accurate, timely, and automated matter. Introduction The primary objectives of this project are to develop an innovative online system for monitoring drilling fluids with a focus on facilitating intelligent control of drilling-fluid performance. The system aims at measuring 10 key parameters continuously, including apparent viscosity, plastic viscosity, yield point, density, pH, and ion concentrations. By incorporating online, real-time, continuous monitoring capabilities, the system offers several advantages, including improved data quality and frequency, reduced on-site labor requirements, and a corresponding decrease in associated health and safety hazards. System Description Overview of the Online Drilling-Fluid-Monitoring System. The online drilling-fluid-monitoring system is available in four distinct configurations: land modular, land skid-mounted, offshore cabin, and indoor small modular, providing customization options to meet specific space requirements. The system is designed to measure 10 crucial parameters essential for drilling operations. These include rheology factors, density, pH, sulfur ion content, and chloride ion content. Density and rheology measurements are performed at a 1-Hz sampling rate, offering near-continuous monitoring. The system is capable of operating within an ambient temperature range from –35 to 50°C, and the testing temperature can be adjusted between 4 and 80°C. Enabled by real-time data transmission, the system allows for the online, real-time monitoring of drilling-fluid properties. The system has proven its utility extensively, with more than 30 sets having been deployed successfully across more than 200 wells. Ideally, the monitoring system is positioned near the circulation tank to enable efficient fluid transfer (Fig. 1a). When space near the circulation tank is limited, an alternative placement can be on the opposite side of the blowout-prevention pipeline (Fig. 1b). However, this alternative may lead to a slight delay in data because of extended circulation times.

A review of risk analysis and accident prevention of blowout events in offshore drilling operations

A review of risk analysis and accident prevention of blowout events in offshore drilling operations

Progress on clay swelling inhibitors: a comprehensive review

Relevance. It is known that swelling of a well wall due to the presence of water negatively affects drilling operations, causing a significant increase in their material costs. The use of various inhibitors prevents this effect and the drilling mud reacting on wellbore narrowing. In this sense, the problems of developing polymer, amino-, ionic liquid and shale inhibitors of surfactants considered in the article are very relevant and of practical interest to oil engineers in terms of the prospects for fundamental and applied researches of these issues. Aim. To study the possibilities of existing approaches and methods that determine the characteristics of the main clay swelling inhibitors, the regularities of changes in the properties of traditional and non-traditional clay materials, as well as the mechanisms of clay swelling with the identification of the most characteristic group of these inhibitors. Object. Processes of minimizing the effect of wall narrowing as a result of exposure to inhibitors. Methods. Systematic, critical and comprehensive analysis of modern data on clay swelling inhibition in the field of drilling muds. Results. The authors have carried out the detailed analysis of clay swelling. The paper discusses the characteristics of inhibitors required to prevent swelling, in the structure of which nitrogen and ionic liquids are contained. It is noted that a high-quality clay swelling inhibitor should include in its base a water-soluble bulk molecule with a distinct hydrophobic and hydrophilic structure; have characteristics, which allow replacing the hydrated cations of the intermediate layer with some hydrophobic cations, such as ammonium ions instead of sodium ions; differ in the ability to disperse multiple cations well. It was found that functional groups that support inhibitor binding to the siloxane groups of the clay are able to significantly expand clay swelling.

Drilling Rate of Penetration Prediction Based on CBT-LSTM Neural Network

Due to the uncertainty of the subsurface environment and the complexity of parameters, particularly in feature extraction from input data and when seeking to understand bidirectional temporal information, the evaluation and prediction of the rate of penetration (ROP) in real-time drilling operations has remained a long-standing challenge. To address these issues, this study proposes an improved LSTM neural network model for ROP prediction (CBT-LSTM). This model integrates the capability of a two-dimensional convolutional neural network (2D-CNN) for multi-feature extraction, the advantages of bidirectional long short-term memory networks (BiLSTM) for processing bidirectional temporal information, and the dynamic weight adjustment of the time pattern attention mechanism (TPA) for extracting crucial information in BiLSTM, effectively capturing key features in temporal data. Initially, data are denoised using the Savitzky–Golay filter, and five correlation coefficient methods are employed to select input features, with principal component analysis (PCA) used to reduce model complexity. Subsequently, a sliding window approach transforms the time series into a two-dimensional structure to capture dynamic changes, constructing the model input. Finally, the ROP prediction model is established, and search methods are utilized to identify the optimal hyperparameter combinations. Compared with other neural networks, CBT-LSTM demonstrates superior performance metrics, with MAE, MAPE, RMSE, and R2 values of 0.0295, 0.0357, 9.3101%, and 0.9769, respectively, indicating the highest predictive capability. To validate the model’s robustness, noise was introduced into the training data, and results show stable performance. Furthermore, the model’s predictive results for other wells achieved R2 values of 0.95, confirming its strong generalization ability. This method provides a new solution for ROP prediction in real-time drilling operations, assisting drilling engineers in better planning their operations and reducing drilling cycles.

Modeling rate of penetration using hybridization of Artificial Neural Network and Artificial Fish Swarm algorithm

This study aims to develop a robust and efficient approach for predicting the Rate of Penetration (ROP) by integrating the Artificial Fish Swarm Algorithm (AFSA) with the Back Propagation (BP) algorithm in an Artificial Neural Network (ANN). The integration of AFSA with Back Propagation is the primary novelty of this work, as it seeks to achieve optimal weight and bias values for the ANN, thereby enhancing prediction accuracy. A dataset of 1944 data points from a vertical well in southern Algeria was utilized. The performance of the developed models was evaluated using correlation coefficient and root mean square error (RMSE). The results demonstrated that the AFSA-ANN model significantly outperforms conventional ANN, Support Vector Regression (SVR), Random Forest regressor (RFR), and Bourgoyne and Young’s model in terms of accuracy. Additionally, the application of leverage method revealed that only 0.98% of the data falls outside the model’s applicability domain, indicating that the data and the model are statistically valid and reliable. The findings of this study have significant implications for the oil and gas field, providing a more accurate and reliable method for predicting ROP, which can lead to more efficient drilling operations and reduced costs.

Three‐dimensional static reservoir modelling of Kareem sandstone reservoir, Tawilla Oil Field, at the southern region of Gulf of Suez, Egypt

The Gulf of Suez, which contains Egypt's oldest oil fields, is one of North Africa's most well‐known oil regions. There are more than 80 conventional oil fields in Egypt's Gulf of Suez, some of which have reservoirs that stretch back to the Precambrian and Quaternary. In close proximity to the southern entrance of the Gulf of Suez is the Tawilla West oilfield. The oil field Tawilla West is believed to consist of rotating fault blocks that descend in a south‐west direction. The main producing reservoirs are the Miocene section reservoirs, the Belayim and Kareem sandstones. The current research is focusing on the structural elements affecting this giant field to update the field structural model using the newly processed 3D seismic survey, the acquired data from newly drilled wells and the associated different logging techniques. The seismic information quality varied from poor to fair. The quality of the interpreted stratigraphic horizons and geological faults was mainly controlled by the seismic information quality. The research used seismic attribute analyses to improve interpretation and incorporate additional features, enabling better hydrocarbon potential identification and characterization of the reservoirs. Several geological structure contour maps and cross‐sections were generated to help in delineating and understanding the reservoir's extension. Based on the detailed correlation study, we were able to detect the faults that affected the structure of the Tawilla West field in detail, define their throw amounts and directions, and identify the missed sections across the studied area. This study introduces an updated model scenario to show the differences and their effect on the field development plan and recommendations. By examining subsurface geologic structural characteristics and evaluating petrophysical data, a 3D static reservoir model was created to resolve structural settings and hydrocarbon trapping, providing detailed information on the field and identifying new opportunities for future development. The research discovered that the updated detailed 3D structural model may support the Kareem Reservoir development plans and encourage drilling, workover and dynamic operations to assign development possibilities in the correct area. According to the established model, there are at least three options in the study's attic areas that might boost oil output and oil reserves for the field while avoiding further failures.

A multi-objective reinforcement learning framework for real-time drilling optimization based on symbolic regression and perception

In the context of the global energy transition, optimizing deep-water oil and gas drilling parameters is crucial for ensuring safety while improving efficiency. Traditional methods face limitations in highly dynamic and nonlinear drilling environments, struggling to balance speed and cost-effectiveness. Furthermore, these methods rely on real-time logging-while-drilling (LWD) data for decision-making, but delays in data collection and processing hinder timely adjustments of drilling parameters, affecting decision accuracy and responsiveness. This paper proposes a multi-objective drilling parameter optimization framework, incorporating symbolic regression, time-series networks, and Markov decision processes to precisely predict ROP, formation conditions, and optimize drilling parameters in real time. Key innovations include a multi-population evolutionary symbolic regression algorithm for constructing empirical equations, the integration of variational mode decomposition (VMD) and sample entropy for data preprocessing, and multi-head self-attention time-series networks to enhance prediction accuracy. Quantile regression further estimates the range of drilling parameter adjustments. Additionally, a drilling parameter optimization deep deterministic policy gradient (DPODDPG) algorithm was developed to automate real-time parameter adjustments. Empirical analysis on the Ledong 10-1 block in the South China Sea demonstrated significant improvements: ROP increased from 54.18 m/hr to 122.17 m/hr, mechanical specific energy (MSE) decreased from 100.82 MPa to 97.78 MPa, and cost per foot reduced from 121.16 × 102 CNY/m to 51.31 × 102 CNY/m. Compared to traditional methods, the proposed framework showed clear advantages in enhancing ROP, reducing MSE, and controlling costs, further validating its superiority in complex drilling environments. This method not only significantly improves drilling efficiency and economic benefits but also adapts to complex and changing drilling conditions, showing broad application potential, particularly in challenging deep-water oil and gas drilling operations, where it can provide more efficient and reliable optimization solutions.

Intelligent Lost Circulation Monitoring Method Based on Data Augmentation and Temporal Models

Deep and offshore drilling operations face complex geological formations, uncertain formation pressures, and narrow safety density windows, making them susceptible to lost circulation risks. To address these challenges, this paper introduces an innovative, intelligent lost circulation monitoring model that incorporates geological lithology information. This model not only utilizes real-time drilling parameters, but also encodes geological information such as rock type as inputs to the model. By combining these key lithological features, the model can comprehensively assess wellbore stability and reduce the lost circulation risks. In this paper, the Conditional Tabular Generative adversarial network (CTGAN) model is used to enhance the data of small-sample risk data, which can effectively expand the data distribution space and improve the performance of the model. This paper conducts a comparative analysis of intelligent monitoring results using artificial neural networks (ANNs), long short-term memory (LSTM), and temporal convolutional networks (TCNs). The results show that the TCN achieves an identification accuracy of 93.7%. Furthermore, the analysis reveals that the inclusion of lithology information significantly enhances the model’s performance, resulting in a 7.1% increase in accuracy. The false alarm rate of the model can be reduced by 10.2%, considering the fluctuation of the logging curve caused by the on/off condition of the pump. This indicates that the introduction of lithology information and the condition of the pump on−off provide advantages in monitoring and identifying lost circulation risks, enabling a more precise assessment of wellbore stability and a reduction in lost circulation incidents. The method of lost circulation monitoring proposed in this paper provides an important safety guarantee for the oil drilling industry.

Drilling Automation Serves as Engine of Change for Risk Prevention Offshore

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper OTC 35137, “Drilling Automation: An Engine of Change for Risk Prevention Offshore,” by Brennan Goodkey, SPE, Andrew Fabo, and Francois Le Buhan, SLB, et al. The paper has not been peer reviewed. Copyright 2024 Offshore Technology Conference. _ This paper delves into the evolving landscape of drilling automation, emphasizing the imperative for these systems to go beyond novelty and deliver quantifiable financial value. The complete paper presents an examination of several offshore drilling operations that exemplify the successful implementation of a closed-loop drilling-automation solution and the positive effect these technologies can have on the bottom line. Introduction In the dynamic landscape of drilling technologies, the conventional discourse on rig mechanization, robotics, and drilling automation is no longer a new topic, having nearly a decade of active discussion, development, and deployment. However, these systems, despite their longstanding presence, are still working toward substantiating their anticipated value. To navigate this challenge successfully, a clear understanding of where and how exactly the deployed solution will deliver value is required. The criteria of success inevitably will vary based on the customer, region, and objective. One of the clearest examples is obvious in the dichotomy between the priorities of high-volume drilling in onshore operations compared with offshore drilling. While the overarching goal of automation remains consistent—maintain safety while maximizing production and minimizing operational expenditures—the path to delivery in these spaces looks quite different. Offshore drilling introduces a myriad of complexities and must prioritize not only efficiency but also risk reduction and the minimization of nonproductive time. The complete paper examines the collaborative journey of several oil and gas organizations to deploy and improve a drilling-automation system aimed at the priorities and challenges of offshore drilling. Definitions The authors offer the following definitions to delineate scope as they describe an integrated system that combines domain drilling automation, rig-floor mechanization, and directional-drilling automation into a cohesive and comprehensive solution: - Domain Drilling Automation—This refers to the process of leveraging sensor data to interpret downhole conditions and react with the appropriate domain response, mimicking an experienced driller’s expertise with high accuracy and precision. - Rig-Floor Mechanization—This is the implementation of automated technologies and robotic systems on the rig floor to limit manual human involvement in tasks situated within the hazardous red zone. - Directional-Drilling Automation—This term refers to the use of automated systems and technologies to control and navigate precisely the trajectory of a drilling borehole, enhancing accuracy and efficiency in achieving specific wellbore paths and target zones. - Planning Drilling Automation—The use of advanced technologies and automated systems during office-based well planning is leveraged to optimize resources, enabling precise and efficient decision-making for the entire drilling process. - Real-Time-Monitoring Drilling Automation—The use of advanced automation technologies can monitor and analyze drilling operations continuously in real time, providing instantaneous feedback and adjustments to optimize performance, efficiency, and safety.

Research on the treatment and secondary fluid mixing technology for oil-containing drilling wastewater in gas fields

Drilling operations in oil and gas fields will generate a large amount of highly polluted and difficult to degrade drilling wastewater. Oil drilling wastewater mainly includes diluted drilling fluid mud, heavy metals and radioactive substances. At present, the treatment methods for oily drilling wastewater mainly include reinjection into the formation, recycling and discharge after standard treatment, but the treatment effect is not good. This article describes the treatment of oily drilling wastewater through wastewater oxidation, flocculation, mechanical dehydration, intermediate water oxidation, flocculation and filtration. The experimental results show that the RR of the wastewater can reach 93.50%, the maximum oil recovery rate (ORR) can reach 89.56%, the light transmittance (LT) of the purified water can reach 98.40%, the suspended solids (SS) can be reduced to 46.12mg/L, the HBO2- content can be reduced to 4.20mg/L. The water quality meets the requirements of DB61/T 1248-2019. Indoor technology was used for on-site testing, and the lowest reduction rate (RR) and ORR after wastewater treatment were 87.64% and 84.21%, respectively. Comparing the cost of treating the same type of wastewater, the treatment cost of the process in this article can be reduced by 1.87 USD/m3, which has higher economic benefits.

Application for Predicting Vessel Motion Enhances Operability in Offshore Drilling

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper OTC 35341, “Enhancing Energy Efficiency and Operability in Offshore Drilling Operations: Validation and Benefits of a Machine-Learning-Based Vessel-Motion-Prediction Application,” by Daniel A.C. Canache, Massimiliano Russo, and Rune Haakonsen, Kongsberg Maritime, et al. The paper has not been peer reviewed. Copyright 2024 Offshore Technology Conference. _ This paper delves into the framework of a new application that leverages advanced machine-learning (ML) techniques in conjunction with metocean forecasts to predict vessel motions and thruster loads. The discussion encompasses the key role of diverse input factors in shaping prediction accuracy and outlines strategies to address inherent uncertainties associated with methods, weather forecasts, and the stochastic nature of the underlying problem. Methods, Procedures, and Process Traditional Physics-Based Vessel Model. A physics-based model of the vessel is used to train the ML model. As such, the accuracy of the physics-based model is critical to the success of the ML model. The model is built using vessel-specific hydrodynamic data, mass data, and thruster-control software. The thruster-control software is extracted from the actual vessel to ensure that the same settings are being used in the model. The model is used to execute time-domain simulations, which include second-order effects, from which prediction targets [six degrees-of-freedom (DOF) motions and thruster usage] are extracted. As shown in Fig. 1, four environmental components are typically considered: wind waves, swell waves, wind, and current. To train an ML model capable of predicting in any given weather condition, environmental parameters are varied over thousands of simulations. Physics-Informed Neural Network (PINN) Architecture. The adopted architecture for the ML model is a PINN, a model architecture that offers several advantages. By integrating vessel-specific hydrodynamic data, the model becomes grounded firmly in the underlying physical phenomena. Furthermore, the architecture facilitates the model’s comprehension and assessment of the environment by enabling the compilation of individual loads into an overall load acting on the vessel. Ultimately, the analysis of resultant loads on the vessel provides a means to compare various environmental load cases directly in relation to the vessel’s response. Optimizing the Training Matrix. The optimal training matrix should span the environmental parameter space up to the vessel’s station-keeping capacity while minimizing the number of necessary load cases. Given the computational expense and prolonged execution times associated with time-domain simulations, it is important to maintain a manageable number of cases. While resultant loads facilitate the comparison across different environment load cases, a systematic algorithm is needed for case selection. To address this requirement, a similarity metric incorporating cosine similarity and Euclidean distance was implemented. The downside to the Euclidean distance is that, at low magnitudes, the distance will be low regardless of direction. This is mitigated to some extent by combining the measure with the cosine similarity. The cosine similarity will be high if two vectors are close in direction regardless of their magnitudes. For the specific problem described in the complete paper (vessel responses), it is desirable that the similarities at low loads stay high because the response also will likely be low.

Preparation of zwitterionic polymer and its application for dual-functional inhibition in deepwater drilling

During deep-water drilling operations, due to the conditions with low temperature and high pressure in the wellbore, methane hydrate can be easily formed in the presence of methane and plug the wellbore. In addition, the deep-water sediments are dominated by clay-silty with weak cementation, so well instability may be occurred due to clay hydration. In order to inhibit hydrate formation and clay hydration in deepwater drilling, a zwitterionic polymer AADV was synthesized through quaternary copolymerization of AM/AMPS/DMDAAV/VAC. The hydrate inhibition performance of AADV was evaluated in a high-pressure reactor chamber, and the results showed that AADV can delay the hydrate formation time from 330 min in pure water to 2204 min, indicating an excellent inhibition effect of hydrate formation. Meanwhile, 1 % AADV can significantly reduce the linear swelling rate of the calcium-based bentonite to 3.69 % for 10 h and increase the rolling recovery rate to 94.7 %, suggesting an excellent inhibition effect on clay hydration. The mechanism of dual-functional inhibition of AADV was further analyzed. The zwitterionic group in AADV and the hydrophilic amide group can strongly adsorb the water molecules through a strong hydration effect and the hydrogen bonding effect, respectively, which contributes to destroy the water molecules cage structure, and thus to suppress the hydrate formation. Besides, AADV can effectively obstruct mass transfer of methane molecules by increasing the viscosity of the aqueous phase and absorbing on the hydrate surface, leading to hydrate formation inhibition. For the inhibition of clay hydration, the SEM and XRD results proved that AADC inhibits the clay hydration through the film-forming effect. In addition, the contact angle of Na-Bent/AADC composite reached to 55.68° when the polymer concentration increased to 2 %, suggesting that the hydrophobicity of Na-Bent surface can be enhanced after AADC treatment. AADV can wrap around clay particles and form hydrophobic film through adsorption and intercalation, preventing the intrusion of external fluids and inhibiting clay hydration.

Technology Focus: Offshore Drilling and Completion (October 2024)

The evolution of computational technology, coupled with the ability to digitize hardware and engage with end users, has enabled machine learning to enhance operational efficiency, safety, and cost-effectiveness in offshore oil and gas development. A key takeaway from recent literature is that, while automation tools have been present for years, their widespread adoption has been hindered by a lack of demonstrable financial benefits. To address this, the focus has shifted from mere deployment to achieving real value through significant cost savings, increased safety, and improved operational efficiencies (paper OTC 35137). Automation efforts in onshore operations prioritize efficiency, while offshore automation must focus on risk reduction and minimizing nonproductive time. For instance, a cutting-edge vessel-motion-prediction tool integrates advanced machine-learning techniques with metocean forecasts to enhance the reliability and safety of offshore drilling operations (paper OTC 35341). Paper OTC 35418 presents the importance of autonomous inflow-control devices in maximizing the production life of wells in deepwater West Africa, taking into account the various challenging reservoir conditions. Another critical insight from the research is the role of human intervention in the deployment of automation systems. Successful deployment requires iterative adjustments and feedback from operators to tailor systems to the specific needs of offshore environments. Collaboration among engineers, operators, and decision-makers is essential in refining these technologies to align them with operational goals and safety standards. Overall, these papers underscore the need for strategic adaptation of automation systems to the specific challenges of offshore environments. The success of predictive machine learning in this context depends on its ability to offer measurable financial benefits, improve safety, and drive operational efficiency. Recommended additional reading at OnePetro: www.onepetro.org. SPE 218473 Performance Optimization of Water-Injection Wells Using Autonomous Outflow Control Device Technology in Offshore Norway by G.F. Kvilaas, AkerBP, et al. SPE 218479 Use of Wired Drill Pipe, Along-String Measurements, and Advanced Logging-While-Drilling Imaging Enhances Wellbore-Condition Understanding and Improves Well Delivery Time, While Telemetry-Optimized Ultradeep Resistivity Measurements Enable Precise Geological Well Placement by Stephen Pink, NOV, et al. SPE 218360 Autonomous Restriction Navigation Through Wellbore in Wireline and Slickline Operations by S. DiPasquale, SLB, et al. OTC 35394 Intelligent Optimization of Ultradeepwater Oil-Transmission Risers Using an Enhanced Graph Neural Network by Hong Zhu, China University of Petroleum-Beijing, et al.

Hydrothermal coupling model between wellbore and permafrost for drilling in arctic cold regions

The abundant oil and natural gas resources in the Arctic cold regions have driven people to conduct drilling operations in these harsh environments. During drilling operations in Arctic permafrost regions, the linear heat source effect generated by the flow of drilling fluid in the wellbore continuously melts the surrounding permafrost, which can lead to wellhead settlement and instability of the wellbore wall, among other accidents. In order to provide guidance for drilling engineering in cold regions, there is an urgent need to study the coupled heat transfer between the wellbore and the permafrost in Arctic drilling. Due to the significant influence of hydrothermal coupling process in permafrost on temperature transfer, conventional wellbore heat transfer models are not suitable. Therefore, this paper proposes a hydrothermal coupling model between the wellbore and permafrost for drilling in Arctic cold regions and analyzes the heat transfer in the wellbore and the hydrothermal processes of the formation using numerical methods. By applying this model, the insulation effects of existing main wellbore temperature control and insulation technologies were studied. The main research findings indicate that drilling process can cause the permafrost around the wellbore to thaw, and the thawed permafrost around the wellbore presents a “horn” shape that expands from the wellhead to the bottom of permafrost layer; Without insulation measures, the volume of thawed permafrost around the wellbore can reach 315 m³ after 7 days of circulation; During the drilling process, moisture migration occurs. Under the effect of gravitational seepage, unfrozen water content in the thawed permafrsot around the wellbore increases from the wellhead to the bottom of the permafrsot layer; Vacuum insulated tubing has almost negligible impact on the temperature of the drilling fluid in the wellbore, while it can reduce the volume of thawed permafrost around the wellbore by 62.5 % to 88 % or even higher; Drilling fluid cooling systems can significantly lower the temperature of the drilling fluid in the wellbore but cannot effectively prevent the thawing of permafrost around the wellbore. Therefore, in practical drilling operations, priority should be given to using high-quality vacuum insulated tubing or combining both methods simultaneously.

Spot-checking machine learning algorithms for tool wear monitoring in automatic drilling operations in CFRP/Ti6Al4V/Al stacks in the aircraft industry

In aircraft manufacturing, where diverse materials, including Carbon Fiber-Reinforced Plastics (CFRP), aluminum, and titanium alloys, are employed, the assembly process heavily relies on creating thousands of holes. These holes accommodate bolts and rivets, facilitating the secure interlocking of structural components within the aircraft fuselage. The proliferation of sensor systems in this domain has led to a substantial increase in data generation during the hole-making process, offering a compelling opportunity to optimize the production system. In this context, this article is dedicated to harnessing the data collected from the production system of a commercial aircraft to refine the assembly process, with a specific focus on reducing consumable costs. The primary approach involves developing a real-time Tool Wear Monitoring System by comparing the performance of Linear Regression, Lasso Regression, Ridge Regression, k-Nearest Neighbors, Support Vector Regression, Decision Tree, Random Forest, and Extreme Gradient Boosting Machine Learning models. Using a scale of the general drill condition as an outcome, the Gradient Boosting Regressor has shown outstanding results. Notably, the residuals consistently exhibited zero-centered errors in training and test sets. However, it suggests that further enhancements are needed to surpass human-level performance in predicting tool conditions because of the quality and quantity of available data.

DEVELOPMENT OF PROJECT DOCUMENTATION FOR WELL CONSTRUCTION WITH THE USE OF MPD TECHNOLOGY

In traditional drilling technology, in order to prevent gas and oil-water blowouts, the pressure in the well should exceed the reservoir pressure by 5–10 % in static conditions, depending on the depth. However, in case of catastrophic sinking, drilling of highly permeable formations, narrow drilling windows in zones of abnormally high pressures such a safety method, on the contrary, often makes it impossible to drill the well. Today there are special systems to control the pressure in the well during deepening and circulation stops for drilling in difficult geological conditions. Their work is based on high-precision control of pressure in the annulus and its regulation at the sealed wellhead by means of an automatic choke in combination with a Coriolis flow meter. In static conditions, the required pressure is maintained either by an additional pump or by a nitrogen injector.Development of well construction projects requires reference to industry specific normative documents regulating the methodology of work, equipment selection and defining the minimum list of other technical and technological solutions necessary for quality and safe drilling operations. However, despite the fact that the technology of drilling with controlled pressure in different variations has been used in Russia for twenty years, there are still no regulatory documents summarizing the entire domestic experience of its application and regulating the procedure of drilling operations with controlled pressure. Their absence leads to certain technical risks and difficulties in passing departmental expert reviews of project documentation.This article analyses the rules and recommendations for well construction using managed pressure drilling technology contained in national industry regulatory documents, highlights the problems arising in the development of the technical part of the design and estimate documentation for the construction of similar wells, and gives recommendations on the composition of a regulatory document that could summarize national and foreign experience of managed pressure drilling.

Dynamics of an offshore drilling tube system based on a riser–drill string–drilling fluid coupling model

ABSTRACT Accurate prediction of dynamic characteristics of an offshore drilling tube system under marine environmental loads is significant to secure the safety of offshore drilling operations. Based on previous works for coupling riser with drill string to form a pipe-in-pipe structure, in this present work, the influence of drilling fluid is taken into consideration to develop a riser–drill string–drilling fluid coupling model; and the corresponding verifications are conducted via comparison with the experimental results. The collisions between riser and drill string are demonstrated to restrict the lateral drifting of the riser, which, however, can be intensified by the upward flow of drilling fluid. In addition, as increasing the offset of the drilling platform, the lateral drifting of the riser also increases; moreover, the position for the maximal drifting moves upwards to the sea surface. When the part of the drill string below the mudline is taken into consideration, the increasing angle of well inclination is illustrated to restrict the lateral drifting of the riser above the mudline.

Influence of HSS drill coatings on surface finish and cylindricity of AA6061/C/ZrO2 composite in CNC drilling operations under dry conditions

Abstract This study aims to evaluate the effect of HSS drill tool coatings, namely single-layer TiN, single-layer AlCrN, and double-layer AlTiN + TiSiXN (Durana), on the surface roughness and cylindricity of AA6061 (90 wt%)/C (5 wt%)/ZrO2 (5 wt%) hybrid composite material processed by the stir casting method. The fabricated sample was examined for the uniform particle distribution of reinforcements using Scanning Electron Microscope. The drilling operation was carried out on the fabricated in a CNC machining center by setting the spindle speeds of 800, 1200, and 1600 rpm, depths of cut of 0.5, 1, and 1.5 mm, and feeds of 50, 100, and 150 mm/rev. An orthogonal array (L27) designed by Taguchi’s method was used as the design of the experiment for the optimization of the best cutting parameters and coating. Surface roughness and cylindricity errors were determined for the 27 experimental runs. Field emission scanning electron microscopic (FESEM) examination and Energy Dispersive Spectroscopy (EDS) were used to analyze the surface integrity and elemental composition, respectively. The results revealed that Durana had a significant effect on the surface substrate with minimum surface roughness (Ra) of 1.666 μm and obtained the minimum cylindricity error for the parameters of depth of cut 0.5 mm, spindle speed of 1200 and feed of 100 mm rev−1.

A New Correlation for Estimating Static Elastic Properties of Sandstone Formations: Case Study from Rumaila Oilfield

Evaluating the elastic characteristics of reservoir's rocks is crucial for the oil and gas sector to reduce risks throughout drilling and production operations. Elastic parameters are essential for prediction of wellbore stability, estimation of in-situ stresses, optimization of drilling performance and design of hydraulic fracturing. Depending on the type of formation, Elastic parameter is varied dramatically. Thus, a precise way to identify these characteristics is needed. Inaccurate assessment of elastic characteristics can result in excessive expenditure and inappropriate field development plans. Static elastic modulus (Est) can be determined through laboratory testing. Although laboratory measurement is the most precise approach to determine the elastic modulus, it requires a continuous core sample to acquire a complete profile of the formation's elastic characteristics. However, just a portion of the well is typically sampled for cores. On the other hand, wireline log data is applied to determine the dynamic modulus (Edyn), allowing for continuous assessment of elastic properties. Numerous characteristics of the rock, including consolidation, pore pressure and lithology affect the dynamic modulus. Thus, it needs to be converted to Static elastic modulus by empirical formulae. Several empirical equations have been proposed to convert dynamic Young's modulus to static Elastic modulus. Nevertheless, the validity of these relationships is restricted and exclusive to certain areas. This research seeks to establish a relationship between Dynamic modulus and static Elastic modulus for sandstone formation of Rumaila oil field. The suggested correlation provides accurate predictions of the static elastic modulus for the complete sandstone segment. The newly created correlation performed better than the previous correlations in predicting static elastic modulus with average absolute error of 8.69 %, and correlation coefficient of 0.9848. The newly established empirical correlation can assist geo-mechanical engineers in estimating continuous profile of the sandstone formation's static modulus.

A data-driven dynamic method of downhole rock characterisation for the vibro-impact drilling system

For the real-time characterisation of an inhomogeneous impact inhibiting constraint such as downhole rock layers, an unconventional method using machine learning (ML) and drill-bit vibrations is investigated. An impact oscillator with one-sided elastic constraint is employed in modelling the bit-rock impact actions. Measurable drill-bit dynamics, such as acceleration, were acquired and processed into features and 2D-images that were later used in developing ML models capable of predicting the stiffness of impacted rock constraint. Explored ML networks include Multilayer Perceptron (MLP), Convolutional Neural Network and Long Short-Term Memory Network. Both simulation and experimental studies have been presented to validate the proposed method while using coefficient of determination (R2) and normalised mean absolute error (NMAE) as the performance metrics of the ML models. Results showed that the feature-based models had better performances for both simulation and experiment compared to the raw signal and 2D-image based models. Aside being simple and computationally less expensive, the feature-based MLP models outperformed other models having R2 values > 0.7 and NMAE values < 0.2 for both simulation and experiment, thus presenting them as the preferred ML model for dynamic downhole rock characterisation. In general, this study presents a new modality to achieving logging-while-drilling during deep-hole drilling operations such as carried out in hydrocarbon, mineral and geothermal exploration.