Offshore Oil Production Research Articles

Chemical Approach to Control Hydrate in Offshore Gas Production Facilities

- Hydrate formation presents a significant operational challenge in offshore oil and gas production, primarily due to the potential formation of hydrate plugs which obstruct fluid flow, thereby posing serious flow assurance risks. Additionally, these solid, crystalline, icelike structures, composed of low molecular weight gases (such as methane, ethane, and propane) encapsulated in hydrogen-bonded water cages, can aggregate into larger masses capable of damaging or rupturing pipelines. Such formations typically occur under the high-pressure and low-temperature conditions prevalent in subsea flowlines and cold-weather operations. This study employs the Prosper simulation software to model these complex thermodynamic and hydrodynamic conditions and to predict the effective dosages of chemical inhibitors required to prevent hydrate formation. Specifically, our simulations suggest optimal dosages of 35% wt. methanol (MeOH) and 45% wt. monoethylene glycol (MEG) for gas stream 1, and 22% wt. MeOH and 33% wt. MEG for gas stream 2. Based on these findings, we advocate the use of Prosper simulation software as a predictive tool for the strategic administration of hydrate inhibitors in offshore gas production facilities. This research contributes to the ongoing development of chemical strategies for hydrate management, providing a basis for improved safety and efficiency in hydrocarbon extraction processes.

A Coupled, Global/Local Finite Element Methodology to Evaluate the Fatigue Life of Flexible Risers Attached to Floating Platforms for Deepwater Offshore Oil Production

A Coupled, Global/Local Finite Element Methodology to Evaluate the Fatigue Life of Flexible Risers Attached to Floating Platforms for Deepwater Offshore Oil Production

Limited increases in Arctic offshore oil and gas production with climate change and the implications for energy markets

Climate change impacts on sea ice thickness is opening access to offshore Arctic resources. The degree to which these resources are exploited will depend on sea-ice conditions, technology costs, international energy markets, and the regulatory environment. We use an integrated human-Earth system model, GCAM, to explore the effects of spatial–temporal patterns of sea-ice loss under climate change on future Arctic offshore oil and gas extraction, considering interactions with global energy markets and emission reduction scenarios. We find that under SSP5, a “fossil-fueled development” scenario, the effects of sea-ice loss are larger for Arctic offshore oil production than gas. Under SSP5, future extraction of Arctic offshore oil and gas through 2100 adds roughly 0.8–2.6 EJ/year to oil and gas markets but does not have large impacts on global oil and gas markets. Surprisingly, a low-carbon scenario results in greater Arctic offshore oil production to offset the more emissions-intensive unconventional oil production.

Robust Adaptive Control of the Offshore Produced Water Treatment Process: An Improved Multivariable MRAC-Based Approach

The application of deoiling hydrocyclone systems as the downstream of three-phase gravity separator (TPGS) systems is one of the most commonly deployed produced water treatment processes in offshore oil and gas production. Due to the compact system’s complexity and tailor-made features, it is always challenging to develop some optimally coordinated control solution for the coupled hydrocyclone and TPGS systems. It is obvious that coordinated control can better fulfill legislative discharge regulation by robustly maintaining high separation efficiencies. This paper presents a new control solution for a set of integrated hydrocyclone and TPGS systems by applying an improved multi-variable model reference adaptive control (MV-MRAC) approach with the aim of achieving both asymptotic output tracking and unknown disturbance rejection. A robust MV-MRAC controller design is proposed based on a control parameterization derived from a factorization of a high-frequency gain matrix Kp=LDS as a product of three matrices, where L represents unity lower triangular, D=sign(D) represents diagonal, and S represents positive definite, and a teaching–learning-based optimization (TLBO) algorithm for optimizing the adaption rates. The developed solution is analyzed and compared with a commonly deployed PI control solution on a model that is derived from a lab-scale produced water treatment process. This simulation study demonstrates the promising potential of the proposed control solution compared with the currently deployed PI control solution.

Combinatorial reasoning-based abnormal sensor recognition method for subsea production control system

The subsea production system is a vital equipment for offshore oil and gas production. The control system is one of the most important parts of it. Collecting and processing the signals of subsea sensors is the only way to judge whether the subsea production control system is normal. However, subsea sensors degrade rapidly due to harsh working environments and long service time. This leads to frequent false alarm incidents. A combinatorial reasoning-based abnormal sensor recognition method for subsea production control system is proposed. A combinatorial algorithm is proposed to group sensors. The long short-term memory network (LSTM) is used to establish a single inference model. A counting-based judging method is proposed to identify abnormal sensors. Field data from an offshore platform in the South China Sea is used to demonstrate the effect of the proposed method. The results show that the proposed method can identify the abnormal sensors effectively.

Reactive power capacity allocation technology of shore‐based power supply system of offshore oil production platform under different network topology reliability measures

AbstractTo improve the economy of offshore oil production platform shore‐based power transmission mode, the reactive power capacity allocation technology of offshore oil production platform shore‐based power supply system based on different network topology reliability measures was proposed. The optimization model of power supply system network topology and the structure model of voltage source converter high voltage direct current transmission system were established, the start and stop control scheme of flexible direct current (DC) transmission system was designed, and the operation parameters of converter station and DC line were monitored in real time. The topological structure of offshore oil production platform shore‐based power supply system is constructed, the mathematical model of flexible DC transmission system is established when the alternating current side voltage is balanced, the reactive power capacity configuration of offshore oil production platform shore‐based power supply system is optimized, and the life cycle cost results are calculated. The experimental results show that the algorithm proposed in this paper can obtain the optimal solution only after 26 calculations, with high optimization efficiency and good convergence effect. Reactive capacity configuration of shore‐based power supply system for offshore production platform can reduce failure rate and life cycle cost.

Enhancing virtual flow metering on offshore oil platforms through parallel computing and data reconciliation

Technological advances in recent years have facilitated data accessibility and increased computational resources; consequently, more remarkable numerical performances in data analysis and computational tests have become possible. In this context, parallel computing techniques are considered relevant tools in applications necessitating reduced computational time, such as online and real-time monitoring of industrial processes. For these reasons, the present work develops and implements online and real-time monitoring of offshore oil and gas production, using parallel computing to improve the performance of the data reconciliation methodology implemented, which will estimate the virtual flow metering with more reliability and in real time. To achieve this, three optimization methods (DA, L-BFGS-B, and SLSQP) and two approaches of parallelization schemes were considered: (1) parallelization of the simulations of each production well; (2) parallelization of the numerical gradient, performed by the deterministic optimization method. Parallel implementations allowed the reduction of up to 80% (type 1) and up to 89% (type 2) of the simulation time compared to their serial counterparts. Hence, the parallel execution of type 2 exhibited more promising results, while type 1 demonstrated greater adaptability to optimization methods. Finally, the results indicate that parallel computing techniques facilitate data reconciliation in real-time, enabling online monitoring in a much shorter time and more accurately than achieved with similar sequential procedures. Moreover, the implemented methodology has proven effective for virtual flow metering of each well’s flow rates of oil, water, and gas. In this context, parallelism techniques present the potential for developing the global monitoring of the virtual flow meters for real-time oil, water, and gas flow rates in each well.

Routing and scheduling of platform supply vessels in offshore oil and gas logistics

In this work, we focus on an operational logistics problem that arises in the offshore oil and gas exploration and production industry. In particular, we aim to design cost-effective routes and schedules for platform supply vessels, which are routinely employed to deliver necessary supplies to the platforms as well as to collect from those platforms used materials that need to be transported back to the onshore base for maintenance, reuse, or discarding. To address the rich-featured routing problem that arises in this offshore logistics application, we introduce a novel mixed-integer linear programming formulation and propose a specialized branch-and-cut algorithm to solve such a model. Furthermore, in order to evaluate our proposed algorithm’s performance, we conduct an extensive computational study using representative benchmark instances inspired by real operational data. The computational results show that our algorithm solved the majority of those instances to optimality, demonstrating its potential for practical use in offshore oil and gas logistics operations.

Influence of temperature on corrosion behavior of N80 Steel in multiple thermal fluid environment

During offshore oil production, O2 could be introduced into the multiple thermal fluid, and the temperature of the wellbore can reach up to 200°C, which may result severe corrosion of tubing. However, the corrosion mechanism of downhole pipes in such aggressive environment remains unclear in the high-temperature and high-pressure CO2-O2-H2O mixed environment. Herein, we investigated the corrosion behaviour of N80 steel with different temperatures in CO2-O2-H2O environment. The influence of temperature on composition and structure of the corrosion product film of N80 steel was characterized by scanning electron microscope, energy-dispersive spectroscopy and X-ray diffraction. The results show that the corrosion rates of N80 steel performed a downward trend with the increase of the temperature, but it increased slightly at 180°C. The corrosion rate of N80 steel was up to 1.6 mm/y at 60°C, owing to the damage to corrosion product film by O2 and reduced its protection at lower temperature. As the temperature increased, the formation of Fe3O4 enhanced the protection of the inner corrosion product film, thereby greatly reducing the corrosion rate. However, the product of (FeCa)CO3 in the corrosion film completely lost its protection at 180°C, and the corrosion rate increased slightly to 0.84 mm/y. With the temperature increased to 240°C the corrosion rate of N80 steel reduced to 0.24 mm/y on account of the formation of dense and complete Fe3O4.

Deep Learning-Based Semantic Segmentation and Surface Reconstruction for Point Clouds of Offshore Oil Production Equipment

Deep Learning-Based Semantic Segmentation and Surface Reconstruction for Point Clouds of Offshore Oil Production Equipment

Power-from-Shore Optioneering for Integration of Offshore Renewable Energy in Oil and Gas Production

Despite the widespread usage of high-voltage alternating current (HVAC) for the connection of offshore wind farms (OWF), its use to power-from-shore (PFS) offshore oil and gas (O&G) production sites is often not feasible. Its limitations for long-distance subsea transmission are usually found at 50–70 km from shore and might be even shorter when compared commercially to a direct-current (DC) alternative or conventional generation. Therefore, this research paper aims to address the standardization of offshore transmission with a particular focus on the high-voltage direct current (HVDC) alternative. While the distance is typically not a limiting factor when using DC, and the voltages used are rather standard, the concept of power envelopes can be quite useful in addressing the high variability of offshore site power requirements and setting a design baseline that would lead to improved lead time. In this article, a full back and front-end genetic optioneering model purposely built from the ground up in Python language is used to #1 define up to three DC power envelopes that would cater to most of the candidate’s requirements and #2 provide the lowest cost variance. The results will demonstrate that this can be achieved at a minor overall cost expense.

A predicting method for the mechanical property response of the marine riser based on the simulation and data-driven models

The accurate and real-time prediction of the mechanical property response for the safety assessment of the marine riser is necessary during offshore oil and gas production. However, a finite element simulation is time-consuming, which obstructs the real-time monitoring and evaluation of the marine riser. Therefore, to obtain the real-time mechanical response of the marine riser, a predicting method based on the simulation and data-driven models is proposed. To obtain the prediction data, the finite element simulation model under the typical working conditions is established by the Abaqus software, and then the sensitivity analysis of influence factors is conducted due to the varied working conditions. The orthogonal experimental method is used to select the typical working conditions of the marine riser, and the selection of the sensitive influence factors is ensured based on the typical working conditions, the range analysis, and the grey relational method. To obtain enough training data, the Latin hypercube sampling method is used to enlarge the data for the analysis process. To find a more reliable prediction algorithm, four typical single surrogate models, which are the polynomial response surface model, the radial basis model, the kriging model, and the support vector machine regression model, are selected to establish a predicting model library. Meanwhile, a combining surrogate model, including the average weight surrogate model and the adaptive weight surrogate model, is established based on the combining weight to further improve the predicting accuracy. As a result, an optimal surrogate model is selected from the above four single and two combining surrogate models based on four evaluation criteria including the mean absolute percentage error, the mean absolute error, the root mean square error, and the goodness of fit. The cases show that the optimal surrogate model has a highly rapid response, with average relative errors are 8.17%, 3.42%, and 11.40% for the maximum bending moment, the maximum equivalent stress, and the maximum lateral deformation, respectively. In the above study, the accurate and reliable prediction method of the mechanical property responses will provide a theoretical basis for ensuring the safety of the marine riser.

CHARACTERIZATIONS AND CLASSIFICATION OF FORMATION WATER PRODUCED IN OILFIELDS

Formation water known as oilfield wastewater, produced water, associated water and brine; which generated in great volumes in onshore and offshore oil and gas production. As more and more world major oilfields mature, more water begins to be produced from oil producers because of aquifer encroachment and/or water injection. The evaluated characteristics of offshore oilfield produced water in oilfields is very important for petroleum engineering industry from both upstream and downstream activities, as well as is essential for both environmental assessment and reservoir management. This study has been conducted on two oilfields namely Sarir and Nafora for oil producing wells to distinguish the characterizations of the produced water. Also, the water characteristics are investigated in five gathering centers in Sarir oilfield. The study included the analysis of sixty water samples to determine the physiochemical characterizations. The data obtained from the chemical analysis were used to classify formation water and to determine the environmental deposition using Sulin's system. The results revealed that the type of formation water in both two fields are belonging to CaCl2 type and deep marine environment. The Stiff diagrams of the studied formation water of Sarir field were constructed for five gathering centers and compared with different patterns of Stiff diagrams. Most of the studied water exhibits a similarity of these patterns, but some of them show slightly deviation, this may be attributed to the type of formation and the source of brine.

An approximated dynamic programming model for the supply vessel fleet sizing problem

The offshore oil and gas exploration and production activities heavily rely on a fleet of supply vessels, which play a crucial role in regularly providing necessary provisions from an onshore supply depot to the offshore installations. For Petrobras, the decision-making process concerning the number of supply vessels to be hired for the upcoming two years holds significant economic implications, given that the daily rates for a single supply vessel can soar to tens of thousands of USDs.In this study, we address the challenges of this heterogeneous fleet sizing problem by presenting an innovative approximated dynamic programming (ADP) model. The model takes into account the uncertainties associated with sea conditions and future demand, making it well-suited for real-world applications. To tackle this problem, we explored two types of approximate value functions: a piecewise linear concave value function and a neural network value function. Both methods underwent rigorous testing and demonstrated their capability to effectively address real-world problems. Consequently, our proposed method exhibits promising potential for practical implementation, allowing companies like Petrobras to accurately assess and determine the ideal fleet composition. By introducing this ADP model, we aim to provide valuable insights and solutions that enhance decision-making processes related to supply vessel fleet sizing in offshore operations. Through considering various uncertainties and complexities, our approach offers a robust and reliable framework to support critical economic decisions in this dynamic industry.

Neural networks informed by physics for modeling mass flow rate in a production wellbore

Optimal control strategies may efficiently manage and operate assets in contemporary oil and gas fields, lowering costs and maximizing yield. However, during this process, we encounter a traditional modeling challenge: obtaining an appropriate system representation. Concerning this task, the current research introduces a new hybrid model for simulating multi-phase mass flow in an offshore oil and gas production field. We investigate a concept based on Physics-Informed Neural Networks (PINN) to characterize the mass flow dynamics in a production wellbore. PINN models have been demonstrated as a feasible alternative for training neural networks using a small number of training data samples and estimating the parameters of physics equations that guide the underlying dynamics. The concept might provide alternatives to modeling and balancing the task of learning from physics and data in oil-gas systems. This research analyzes the performance of neural networks inspired by physics in a synthetic database constructed from the physics laws of mass conservation and conservation of momentum. After solidifying the principles, the study assessed the power of model assimilation in a real-world oilfield situation.

Effectively scaling assessment efforts in the evaluation of environmental risks of offshore produced water discharges using a tiered approach

For offshore oil and gas production facilities, overboard disposal of treated produced water (PW) remains a principal waste management option, and regulatory frameworks have been established to manage these discharges. Risk-based assessment (RBA) of PW, which aims to quantify potential impacts on the environment, has been adopted in some regulatory systems, but is also applied by oil and gas operators as part of their risk management systems. In 2020, the International Association of Oil and Gas Producers published a guidance document, including a four-tiered assessment framework aiming for a broader understanding and consistent application of PW RBA globally. The current study reviewed case studies and categorised them along the proposed assessment tiers. Regulatory developments are shown to be key drivers for the application of PW RBA globally, but required tools are often to resource intense for early risk screening. The proposed framework promotes the use of existing data, read-across techniques, and non-animal testing in two initial screening tiers, characterised by little to no sampling. In addition, it allows to refine higher tier assessments through the application of species sensitivity distributions and the inclusion of exposure time to avoid conservative assessment factors. This enables assessments to better approximate observations from field verification, included as a final tier to determine the level of discernible effects. The proposed tiered assessment approach can be considered a novel way to scale assessment efforts to the situation especially in cases where resources are limited, priorities for further investigations need to be set, rapid evaluation of hazard levels is required, sampling logistics and transport of large volumes is complicated and/or there is an opportunity for reduced animal testing. This makes early assessments more effective and ensures logical and efficient actions are triggered for understanding and managing environmental risks of produced water discharges.

Weather the storms? Resilience investment and production losses after hurricanes

This paper studies whether resilience investment mitigates damages from extreme weather events using data on offshore oil production in the Gulf of Mexico. We show that hurricanes which pass near rigs lower production and that stronger storms have larger impacts which persist for months. Regulatory changes that required rigs be designed to be resilient to major hurricanes only modestly mitigated the short-run production losses from hurricanes, reducing oil production losses by roughly 5% at 1 month and 20% 8 months after impact. They also only partly mitigated long-run losses, lowering the probability that a rig permanently exits production by 12–18 percentage points after a hurricane.

Analysis of Critical Knowledge for Strengthening Resilience and Operational Safety

Resilience, viewed from an engineering perspective, refers to a system's inherent ability to regulate its operation before, during, and after disruptions. Knowledge is a critical resource that enables resilient responses, as it is essential for responding, monitoring, anticipating unforeseen events, and learning from their occurrences. However, despite the importance of knowledge in promoting resilience in operational safety, there is a lack of tools in the literature that provide evidence and explanation of knowledge resources. To address this gap, this article presents the development stages of an instrument based on the Critical Decision Method to elicit critical knowledge in Operational Safety Events (OSEs). The instrument includes a set of questions that guide the identification of decision-making points and the exploration of knowledge resources mobilized at each stage, founded on situational awareness, resilience engineering, and knowledge engineering. Prospective analysis sessions and retrospective OSE analyses were conducted with teams of workers from offshore oil and gas production and exploration platforms to assess the instrument's feasibility. The instrument enabled the identification of critical knowledge in both scenarios, which served as valuable input for promoting security and resilience. The instrument also facilitated the identification of opportunities to promote organizational learning and the development of effective actions to strengthen intangible resources that influence resilient responses, thereby enabling a thorough exploration of knowledge resources in retrospective analyses or prospective sessions of accidents and severe operational safety events.

Safety Evaluation Method for Submarine Pipelines Based on a Radial Basis Neural Network

As the lifeline of offshore oil and gas production, a submarine pipeline requires regular safety evaluations with proper maintenance according to the evaluation results. At present, the safety factors based on regional-level commonly used factors in engineering are too many, and this leads to conservative evaluation results with a low acceptance of defects. In this paper, a risk factor evaluation index system for submarine pipeline defects is constructed through an analytic hierarchy process (AHP), and the original safety factors are corrected to achieve accurate evaluations for submarine pipeline safety. By constructing a radial basis neural network (RBFNN), the fast calculation of safety factors for other pipeline defects can be realized. Through comparison, it was found that the values obtained by the machine training were in good agreement with the real values, which reflects the accuracy of the model and provides a basis for the repair of a defective pipeline.

Coupling effect of vortex-induced vibration and local scour of double tandem pipelines in steady current

Submarine pipelines are critical for offshore oil and gas production. However, when these pipelines are located on the seabed, they are susceptible to simultaneous occurrences of local scour and vortex-induced vibration, which impose a significant threat to pipeline safety and integrity. To address this issue, our study investigates the coupling effect of vortex-induced vibration and local scour using Flow-3D. Specifically, we explore the influence of the gap ratio on the vibration and local scour of the pipelines. Our findings reveal that the horizontal gap between the pipelines significantly affects the depth and profile of the scour pits. When the horizontal gap ratio is G/D = 2, pipeline vibrations promote the merging of scour pits. And as the interference effect between the two pipelines weakens with the increase of horizontal gap, the depth difference of scour pits below the fixed and vibrating double pipelines gradually decreases. Moreover, when G/D ≥ 3, the vortices falling off the upstream and downstream pipelines are discharged separately. This results in weak interference between the pipelines and the formation of two independent scour pits.