Drilling Platform Research Articles

Hazard Identification of Well Test Operation in Drilling and Production Offshore Platform by HAZID

A significant portion of energy resources has been discovered in offshore sectors, leading to a continuous increase in the volume of activities and operations. Once a well is drilled and fluid extraction begins, all the reservoir parameters, start to change. well test operation is one of the most crucial tools for engineers to comprehend the behavior and parameters of hydrocarbon reservoirs. In this study, the hazards associated with the well test operations has been identified by using HAZID technique. A total of 189 risks were identified in the initial risk assessment, with 35 categorized as low risk, 88 as medium risk, and 66 as high risk and after implementing protection layers in the secondary risk assessment, the number of low risks incidents increased, while medium and high-risk incidents were reduced significantly. Most of the risks identified are associated with loading operations and sea transportation from the port to the drilling rig. Given that loading and unloading operations are among the most frequently repeated activities in well testing operations, it is determined that they play a critical role in the overall risk profile

Research on Nondestructive Testing Technology for Drilling Risers Based on Magnetic Memory and Deep Learning

Drilling risers play a crucial role in deepwater oil and gas development, and any compromise in their integrity can severely hinder the progress of drilling operations. In light of this, efficient and accurate nondestructive testing of drilling risers is paramount. However, existing inspection equipment falls short in both efficiency and accuracy, posing challenges to the sustainability of deepwater oil and gas exploration and development. To effectively assess the damage conditions of deepwater drilling risers, this study developed an inspection robot based on metal magnetic memory and researched intelligent defect recognition methods using computer vision. The robot can perform in situ inspections on drilling risers and has been successfully deployed for field application on a deepwater drilling platform. The application results demonstrate that this detection robot offers significant advantages regarding high reliability and detection efficiency. Utilizing data collected on-site, we constructed a dataset containing 1100 images that cover five typical types of defects in drilling risers, including pitting, groove corrosion, and wear. Based on this dataset, we proposed and trained a novel image classification model, SK-ConvNeXt-KAN. By deeply optimizing the ConvNeXt convolutional network incorporating the introduced SK attention module and replacing traditional linear classification layers with the KAN module, this model significantly enhanced its feature extraction capabilities and efficiency in handling complex nonlinear problems. Experimental results show that this model achieved an accuracy rate of 95.4% in identifying defects in drilling risers, which is significantly better than traditional methods. This achievement has dramatically improved the efficiency and accuracy of deepwater drilling riser inspections, providing robust technical support for deepwater oil and gas exploration and development sustainability.

Simulation of the impact of an ultrasonic drill as a composition of a robotic platform for investigation of soil on celestial bodies

The article discusses the theoretical issues of developing an ultrasonic drill for drilling soil on celestial bodies as part of a robotic platform and formulates the tasks of its control. The specifics of using the drill require a wide temperature range during operation, low energy consumption, an efficient operating cycle and the requirement to ensure the preservation of the structure and composition of the soil when exposed to the tool. One of the promising areas of development is devices with vibration decoupling from the ground by ultrasound due to the transformation of ultrasonic vibrations into repeated impacts of sound frequency using a specially introduced “free” body between the ultrasonic and impact parts of the drill (bit). This allows continuous drilling regardless of the condition of the impact bit, including jamming. The purpose of the work is to select the most effective drill design and compile a mathematical model that provides simulation modeling. Materials and methods. Technical solutions of both domestic and foreign authors were considered, and a prototype was selected. Theoretical works were analyzed and it was found that the materials presented in them do not contain detailed mathematical models necessary for simulation modeling at the stage of design and optimization of the structure. In the theoretical part of the article, mathematical models are proposed, obtained on the basis of equivalent parameters found from a distributed description of the drill design elements. Methods of mathematical physics, classical mechanics and similarity theory were used. The results are that two alternative mathematical models are developed. One of the models has a continuous description with a significantly nonlinear rigidity of the “wall” upon impact, while the impact motion is considered as a type of self-oscillation. The second model is based on the theory of impact. Conclusion. A parametric study was carried out and the dependences of the oscillation frequency of a free body on the size of the impact gap, body mass and amplitude of oscillations of the concentrator, etc. were obtained. The considered models can be used for simulation modeling when developing a drill design. The tasks of controlling a robotic drilling platform are formulated from the position of ensuring the operability of the drill when drilling various soils.

Small-size temperature/high-pressure integrated sensor via flip-chip method

Hydraulic technology with smaller sizes and higher reliability trends, including fault prediction and intelligent control, requires high-performance temperature and pressure-integrated sensors. Current designs rely on planar wafer- or chip-level integration, which is limited by pressure range, chip size, and low reliability. We propose a small-size temperature/high-pressure integrated sensor via the flip-chip technique. The pressure and temperature units are arranged vertically, and the sensing signals of the two units are integrated into one plane through silicon vias and gold–gold bonding, reducing the lateral size and improving the efficiency of signal transmission. The flip-chip technique ensures a reliable electrical connection. A square diaphragm with rounded corners is designed and optimised with simulation to sense high pressure based on the piezoresistive effect. The temperature sensing unit with a thin-film platinum resistor measures temperature and provides back-end high-precision compensation, which will improve the precision of the pressure unit. The integrated chip is fabricated by MEMS technology and packaged to fabricate the extremely small integrated sensor. The integrated sensor is characterised, and the pressure sensor exhibits a sensitivity and sensitivity drift of 7.97 mV/MPa and −0.19% FS in the range of 0–20 MPa and −40 to 120 °C. The linearity, hysteresis, repeatability, accuracy, basic error, and zero-time drift are 0.16% FS, 0.04% FS, 0.06% FS, 0.18% FS, ±0.23% FS and 0.04% FS, respectively. The measurement error of the temperature sensor and temperature coefficient of resistance is less than ±1 °C and 3142.997 ppm/°C, respectively. The integrated sensor has broad applicability in fault diagnosis and safety monitoring of high-end equipment such as automobile detection, industrial equipment, and oil drilling platforms.

Task offloading with enhanced Deep Q-Networks for efficient industrial intelligent video analysis in edge–cloud collaboration

With the rapid development of 5G and IoT technologies, industrial video surveillance systems face increasingly significant challenges in processing large-scale data. Traditional cloud-centric video analysis methods often lead to processing delays and inefficiencies in resource allocation due to centralized data processing. In this context, the emergence of edge computing, especially in edge–cloud collaborative intelligent video analysis, offers an innovative solution. Within this architecture, task offloading strategies focus on intelligent computation task distribution to minimize latency and optimize resource utilization. However, existing methods frequently overlook key application-specific metrics in video analysis, such as accuracy and timeliness. This study introduces a novel Edge–Cloud Collaborative Industrial Video Intelligent Analysis Architecture (ECIVA) based on the GRU-Enhanced Deep Q-Network (GEDQN). This architecture integrates the strengths of Gated Recurrent Units (GRU) for processing sequential data with the efficiency of Deep Q-Networks in optimizing task-offloading decisions. By incorporating a token bucket mechanism, the architecture optimally manages the task offloading rate within the constraints of limited network bandwidth, thus ensuring high accuracy and real-time responsiveness in video analysis. Empirical evaluation with offshore drilling platform video data shows that the architecture achieves 87.5% mean Average Precision (mAP) at a 0.5 IoU threshold and increases system throughput by 39.2% over traditional cloud-centric methods.

Enhancing disturbance rejection in offshore drilling platforms: a dynamic positioning control scheme using equivalent-input-disturbance method with separated observers

This paper presents a dynamic positioning control scheme for offshore drilling platforms using the equivalent-input-disturbance (EID) method with separated observers. The scheme incorporates observer-based disturbance compensation, wave-filtering state estimation, input transformation, and state feedback control to achieve robust control performance. The main contributions include the design of an EID scheme with separated observers, enabling enhanced disturbance-rejection performance, incorporating a notch filter in the EID estimator to reduce energy loss, and giving an observer tuning scheme to achieve a balanced performance between disturbance rejection and wave filtering. Simulation results demonstrate the effectiveness of the proposed scheme. This work provides valuable insights into designing stable and efficient dynamic positioning systems for offshore drilling platforms.

A method for the fatigue-life assessment of subsea wellhead connectors considering riser wave-induced vibration

During drilling operations, strong non-linear environmental loads are applied to the riser, which can generate powerful fatigue loads, resulting in safety hazards. This paper deals with the problem of the 330 m subsea wellhead connector in the Liu-hua 11–1, which is subjected to long-term non-linear fatigue loading in drilling conditions. Examining the fatigue response law of the subsea wellhead connector ensures the safe operation of offshore oil and gas wells. First, fatigue tests were performed on key components to obtain the stress-fatigue life curve. The F22 strain fatigue life curve was constructed based on the Neuber stress-strain relationship theory and Manson-Coffin strain-life equation. Then, the overall drilling platform - riser - subsea wellhead - soil model was established, and the bending moment-stress data of the subsea wellhead connector was obtained via finite element analysis. Then, the fatigue load spectrum was determined via the MATLAB program using the rainflow counting theory. The response characteristics of the wellhead connector fatigue damage were also compared based on four fatigue damage accumulation theories. A set of fatigue calculation and analysis methods for subsea wellhead connectors was finally developed. This method can provide reference and guidance for subsequent studies.

Fault diagnosis of three-cylinder mud pump based on transfer learning

Mud pumps serve as vital components within the circulating system of oil drilling platforms, primarily facilitating the circulation of drilling fluid. With the rapid advancement of deep learning technology, there has been a growing focus on fault diagnosis techniques for mud pumps based on deep learning methodologies. However, existing deep learning approaches often struggle with fault diagnosis of mud pumps under varying operational conditions, as adjustments to the working conditions are necessary in real-time based on drilling depth. To address this challenge, this study introduces an enhanced transfer learning method for diagnosing faults in mud pumps across different operating conditions. Initially, the collected vibration data undergoes resampling to standardize frequency, followed by the utilization of the short-term autocorrelation method to discern phase information of signal impact. Leveraging this phase information, the signal is segmented into distinct segments with uniform phases, thereby minimizing distribution discrepancies between the source and target domains. Subsequently, the transformer is employed as a feature extractor for the model. Finally, a deep sub-domain adaptation network is employed to facilitate transfer from the source domain to the target domain. Validation of the proposed method was conducted using an experimental dataset, with results demonstrating its efficacy compared to other contemporary approaches.

Microstructural Investigations of Weld Deposits from Manganese Austenitic Alloy on X2CrNiMoN22-5-3 Duplex Stainless Steel

Duplex stainless steels are materials with high performance under mechanical stress and stress corrosion in chloride ion environments. Despite being used in many new applications such as components for offshore drilling platforms as well as in the chemical and petrochemical industry, the automotive industry, etc., they face issues of wear and hardness that limit current applications and prevent the creation of new use opportunities. To address these shortcomings, it is proposed to develop a hardfacing process by a special welding technique using a universal TIG source adapted for manual welding with a pulsed current, and a manganese austenitic alloy electrode as filler material. The opportunity to deposit layers of manganese austenitic steel through welding creates advantages related to the possibility of achieving high mechanical characteristics of this steel exclusively in the working area of the part, while the substrate material will not undergo significant changes in chemical composition. As a result of the high strain hardening rate, assisted mainly by mechanical twinning, manganese austenitic alloys having a face-centered cubic crystal lattice (f.c.c) and low stacking fault energy (SFE = 20–40 mJ/m2) at room temperature, exhibit high wear resistance and exceptional toughness. Following cold deformation, the hardness of the deposited metal increases to 465 HV5–490 HV5. The microstructural characteristics were investigated through optical microscopy (OM), scanning electron microscopy (SEM), energy-dispersive X-ray analysis (EDX), X-ray diffraction (XRD), and Vickers hardness measurements (HV). The obtained results highlighted the feasibility of forming hard coatings on duplex stainless steel substrates.

Safety assessment of marine high-end equipment based on evidential reasoning approach under fuzzy uncertainty

Marine high-end equipment reflects a country’s comprehensive national strength. The safety assessment of it is very important to avoid accident either from human or facility factors. Attribute structure and assessment approach are two key points in the safety assessment of marine high-end equipment. In this paper, we construct a hierarchical attribute structure based on literature review and text mining of reports and news. The hierarchical attribute structure includes human, equipment, environment and management level. The correlations among these attributes are analyzed. The assessment standards of attributes are described in details. Different evaluation grades associated with attributes are transformed to a unified one by the given rules. As for the assessment approach, the evidential reasoning approach is applied for uncertain information fusion. Group analytical hierarchical process is used to generate attribute weights from a group of experts, where process aggregation method and result aggregation method are combined in a comprehensive way. The importance of expert is computed by the uncertainty measure of expert’s subjective judgment. A drilling platform is finally assessed by the proposed attribute structure and assessment approach to illustrate the effectiveness of the assessment framework.

Appropriateness Evaluation Study of Land Reclamation of Pre-drilling Project in Southwest China Under Dual-carbon Background

Cultivated land and forest land are important carbon sink systems in the context of dual-carbon, so it is necessary to do a suitability evaluation study of pre-drilling project land reclamation to support the restoration of carbon sink function of the temporarily occupied farmland. In this paper, the suitability evaluation model of pre-drilling engineering land reclamation was established by adopting hierarchical analysis method and TOPSIS multi-attribute decision-making method, and the suitability evaluation study of pre-drilling engineering land reclamation was carried out on a drilling platform in Southwest China.

Identification method for safety hazard behavior in offshore drilling operators

During offshore drilling operations, both human inexperience and fatigue can contribute to drilling stagnation, overflow, blowout, and other accidents. Despite significant advancements in the automation of equipment and facilities on offshore drilling platforms, humans continue to play a central role in drilling operations. Utilizing the TPE-LightGBM model, a method for identifying safety hazard behavior among offshore drilling operators based on eye movement data was proposed. The main contributions of the present study are as follows: Firstly, the application of eye-tracking technology to identify safety hazards among offshore drilling operators overcomes the lack of risk perception and monitoring methods for mitigating the safety hazards experienced by operators. Secondly, a method utilizing composite hotspot mapping for eye movement to determine sensitive area divisions during offshore drilling operations was proposed. This method aims to reduce the influence of irrelevant, missing, and incorrect eye movement data on recognition results. Thirdly, in conjunction with qualitative selection and analysis of eye movement laws, a feature selection method for eye movement in the context of safety hazard behavior in offshore drilling was proposed. This method enhances the efficiency of recognizing safety hazard behavior. Lastly, the TPE algorithm was introduced to enhance the LightGBM model, addressing the issue of inaccurate identification of safety hazard behavior in offshore drilling operators. The results indicate that the proposed method effectively enhances the accuracy and execution efficiency for identifying safety hazard behavior among offshore drilling operators.

Research on deep learning-based point cloud semantic segmentation for offshore drilling platforms

Offshore drilling platforms (ODP) require effective deformation monitoring and damage detection in the wellhead space for operational maintenance. LiDAR technology, replacing traditional methods, plays a key role in damage detection through 3D reconstruction, and the accurate semantic segmentation of point clouds is fundamental to this process. Following advancements in deep learning, neural network models have emerged as effective tools for the semantic segmentation of point clouds in a range of settings. ODP point cloud data, characterized by large volumes, high density, class imbalance, and complex structures, present unique challenges for feature extraction. To address these challenges, this paper proposed a multi-scale bounding box (MSB-Box) feature extraction strategy based on spatial relation dynamic encoding (SRD-encoding) and a multi-scale point cloud down-sampling (MPDS) strategy to enhance computational efficiency. Utilizing these proposed strategies, we developed an ODP-Net, a deep learning neural network based on the attention mechanism. The ODP-Net's semantic segmentation performance was evaluated using a self-constructed ODP scene point cloud dataset and the S3DIS dataset. The results demonstrate that ODP-Net, based on MPDS and MSB-Box, outperforms other networks in diverse scenes, confirming its high accuracy and wide applicability in the semantic segmentation of point cloud data in ODP scenes.

Research and Optimization of Tubular Column Joint Identification Technology for Intelligent Drilling Platforms

Research and Optimization of Tubular Column Joint Identification Technology for Intelligent Drilling Platforms

Hybrid Energy Holds Promise for Artificial Lift for Offshore Brazilian Fields

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper OTC 32671, “Technical Evaluation of the Use of Hybrid Energy (Solar and Offshore Wind) To Supply Artificial Lift Pumps on an Oil Platform on the Equatorial Margin,” by W.K. Silva, Ana L.R. Cunha, SPE, and Ana C. Alves, Federal University of Pará, et al. The paper has not been peer reviewed. _ The complete paper investigates the relevance of wind and solar energy for the supply of artificial lift systems on offshore platforms in the Brazilian equatorial region. The scope of the authors’ study encompassed a detailed technical and environmental assessment, with an emphasis on the integration of a 10-MW floating solar photovoltaic (FSPV) power system to meet demand for 10 pumps. The results obtained highlight the high viability of the system in meeting the demand of these pumps and ensuring an adequate and sustainable energy supply. Area of Study The equatorial margin is an area in northern Brazil covering part of the sedimentary basins of the equatorial Atlantic. The region is home to important sedimentary basins such as the Amazon Basin, the Pará-Maranhão Basin, the Barreirinhas Basin, the Potiguar Basin, and the Ceará Basin, all known for their hydrocarbon reserves. Offshore oil-exploration activities in the equatorial margin involve the installation of drilling and production platforms that include artificial lift systems. To power these systems, a reliable and continuous power source is required. The use of green energies such as wind and solar can be advantageous in meeting this purpose. For the present study, the coastal city of Salinópolis in the state of Pará was selected because of its plentiful meteorological data and its location in the Pará-Maranhão Basin region. Methodology The methodology of the work consists of the evaluation of the energy required for artificial lifting systems with 10 pumps, using the nine steps developed by Baker Hughes’ Artificial Lift Research and Technology Center for the calculation of the power required to drive the pumps. Additionally, the study uses the System Advisor Model (SAM), November 2022 version, developed by the National Renewable Energy Laboratory to simulate the FSPV. Information related to wind potential was obtained through the measuring stations of the National Institute of Meteorology (INMET).

Cementation breaking and grit separation characteristics of weakly cemented natural gas hydrate by a new structure hydrocyclone

Solid fluidization exploitation technology is one of the most promising methods for the safe exploitation of shallow natural gas hydrates (NGH) in the deep sea. However, after primary jet breaking in the solid fluidization exploitation, the hydrate and argillaceous silt particles are in a state of weak cementation and agglomeration. When the argillaceous silt particles enter the offshore drilling platform with the drilling fluid, it causes substantial sand production, high energy consumption, and pipeline blockage, considerably affecting the development process of the NGH test. Therefore, in-situ desanding has become one of the key technologies of solid fluidization exploitation. In this study, a coupling method of hydrocyclone cementation breaking and hydrocyclone desanding was proposed, and a new structure of cementation breaking and grit separation hydrocyclone was designed. In a lab-designed cementation breaking and grit separation hydrocyclone system, the cementation breaking and grit separation characteristics of weakly cemented NGH similar materials in the swirl flow field were studied. The experimental results under the operating conditions studied show that the swirl flow field exhibits good cementation breaking and grit separation performance for weakly cemented NGH similar materials. For weakly cemented NGH similar materials with a compressive strength of 0.06–0.42 MPa, the cementation breaking efficiency of the cementation breaking and grit separation hydrocyclone is in the range of 27.17–99.61%. After the cementation breaking, the mixture of hydrate similar materials (hydrophilic polyethylene powder) and quartz sand was separated in the hydrocyclone. The separation efficiency of hydrate similar materials is 27.59–99.64%, and the desanding efficiency is 87.66–99.98%. The cementation breaking and grit separation of weakly cemented NGH similar materials were successfully realized. Based on the experimental data, a multiple regression model for the cementation strength of similar materials and the swirl flow field strength was established using the least squares method. This method is expected to provide new technical support for in-situ desanding and safe exploitation of the deep-sea shallow NGH production process.

Intelligent Situation Awareness Based on the Fractal Dimension and Multidimensional Reconstruction

AbstractIn order to realize autonomy and intelligence for situation awareness, this paper proposes an intelligent situation awareness model based on fractional dimension information mining and multidimensional information reconstruction. First, some new concepts are proposed, the spatial situation perception is established by 3D reconstruction of the input fusion information, 4D reconstruction completes the situation comprehension and 5D reconstruction seeks the situation prediction. The three‐level situation estimation model is optimized to a more robust flexible model. Combined with the database system, the reasoning learning mechanism, and the diversified human‐machine interface concept, a basic framework of intelligent situation awareness is constructed. Second, the basic process of intelligent situation awareness is provided. Third, based on the flexible configuration method of the situation awareness system and the concept of unmanned weight determination for the situation awareness agents, an efficiency measurement model for the intelligent situation awareness model is also constructed, and an evaluation method for the consistency of multinode intelligent situation awareness is also provided. Fourth, the paper gives a typical electromagnetic situation estimation example for a drilling platform to explain and validate the concepts. Finally, several suggestions are put forward for the next construction of an intelligent situation awareness system.

Disturbance rejection of offshore drilling platforms: An equivalent-input-disturbance-based dynamic positioning control method

Offshore drilling platforms are exposed to wind, waves, currents, and other unknown disturbances. Accurately estimating and rejecting these disturbances is the key to ensuring reliable station-keeping of the platforms. In this study, a novel dynamic positioning method using an improved equivalent-input-disturbance (EID) approach is proposed for offshore drilling platforms. An improved EID estimator is employed to estimate and suppress unknown disturbances, significantly enhancing the disturbance-rejection performance of the dynamic positioning system. The input channels are decoupled through linear transformation, and the parameter tuning process of the observer and controller is optimized, thus improving system performance. The bounded-input bounded-output stability of the closed-loop system is proved. This study provides insights into the design of dynamic positioning systems for offshore drilling platforms.

Application of Mode Superposition Method in the Recoil Response of Deepwater Drilling Risers after Emergency Disconnection

Summary How to effectively control the recoil response after an emergency disconnection is one of the core technical problems in ensuring the safe and reliable operation of a deepwater drilling riser. Currently, the theoretical analysis is based on a discretization model or numerical simulation, which ignores the continuity of the riser system and the coupling effects of load acting on the riser. To address this problem further, in this paper, we establish a mechanical model and control equation with infinite degree of freedom for riser recoil response, where the heave motion of the floating drilling platform, seawater damping, and the viscous resistance of drilling fluid discharge were taken into account. In addition, the correctness of the model and solving approach are verified against the Orcaflex software. On this basis, the influence of wave period, wave height, initial phase angle, and tension coefficient on the recoil characteristics are discussed. The success of riser emergency disconnection is related to the clearance between the lower marine riser package (LMRP) and the blowout preventer (BOP) and the axial force distribution of the riser. The influence of the above-mentioned factors on the riser recoil response is also complicated. On the basis of the assumptions put forward and the model established, some quantitative conclusions are drawn. This study is of reference significance for safety control of riser emergency disconnection operation.

Safety analysis of fire evacuation from Drilling and Production Platforms (DPP)

Offshore platforms are vulnerable to fire accidents and it is essential to conduct safety analysis in case of an evacuation and take measures to improve worker evacuation efficiency and reduce casualties. Based on the proposed Credible and Representative Fire Scenarios (CRFS) methodology, nine fire scenarios were selected and simulated. The safety analysis of fire evacuation was performed based on the coupled injury model and the modified social force model, while the effects of wind direction, wind speed, fire location and pre-evacuation time on the probability of death (POD) were investigated. The results demonstrated that the wind from the stern led to a higher coverage rate of smoke (CRS) on the decks and higher POD values. The wind from the bow, larboard and starboard sides was shown to be more conducive to the dissipation of smoke. As a result, the overall POD values were relatively low. The magnitude of wind speed affects the accumulation behavior of smoke between different decks, and the lower the wind speed, the higher the CRS on the decks and POD of the workers.The pool fire located on the lower deck was more likely to cause injuries than the fire on the working deck. In addition, it was found that the trend between POD and pre-evacuation time shows a non-linear relationship, thus the scenario with the most severe fire threat was used as an example for regression analysis. An approach was proposed to determine the fire-evacuation safety time windows in the event of a fire accident and the time windows in different areas of the nine selected scenarios were predicted. Finally, the time windows obtained from the regression curves and the proposed method were compared.