Offshore Gas Platforms Research Articles

Reducing Emissions in the Maritime Sector: Offshore Wind Energy as a Key Factor

The maritime environment is the setting for a variety of economic activities, such as offshore wind energy, aquaculture, salt extraction, and oil and gas platforms. While some of these activities have a long-term presence, others require decarbonization as they head towards their demise. In this context, the aim of this study is to develop a methodology to replace the electrical energy from offshore high-emission industrial processes with clean electricity generated by offshore wind energy. The proposal is structured in three phases: initiation, which involves the collection of quantitative, technical, and geospatial information of the study area; indicators, where the main indicators are calculated, and the best alternative is selected using multi-criteria evaluation methods; and finally, short-, medium-, and long-term scenarios are proposed. The methodology is evaluated in Spain, and the best alternative, which has a nominal power of 225 MW, is capable of avoiding up to 1.44 MtCO2 by 2050.

An Improved Convolutional Neural Network for Pipe Leakage Identification Based on Acoustic Emission

Oil and gas pipelines are the lifelines of the energy market, but due to long-term use and environmental factors, these pipelines are prone to corrosion and leaks. Offshore oil and gas pipeline leaks, in particular, can lead to severe consequences such as platform fires and explosions. Therefore, it is crucial to accurately and swiftly identify oil and gas leaks on offshore platforms. This is of significant importance for improving early warning systems, enhancing maintenance efficiency, and reducing economic losses. Currently, the efficiency of identifying leaks in offshore platform pipelines still needs improvement. To address this, the present study first established an experimental platform to simulate pipeline leaks in a marine environment. Laboratory leakage signal data were collected, and on-site noise data were gathered from the “Liwan 3-1” offshore oil and gas platform. By integrating leakage signals with on-site noise data, this study aimed to closely mimic real-world application scenarios. Subsequently, several neural network-based leakage identification methods were applied to the integrated dataset, including a probabilistic neural network (PNN) combined with time-domain feature extraction, a Backpropagation Neural Network (BPNN) optimized with simulated annealing and particle swarm optimization, and a Long Short-Term Memory Network (LSTM) combined with Mel-Frequency Cepstral Coefficients (MFCC). Corresponding models were constructed, and the effectiveness of leak detection was validated using test sets. Additionally, this paper proposes an improved convolutional neural network (CNN) leakage detection technology named SART-1DCNN. This technology optimizes the network architecture by introducing attention mechanisms, transformer modules, residual blocks, and combining them with Dropout and optimization algorithms, which significantly enhances data recognition accuracy. It achieves a high accuracy rate of 99.44% on the dataset. This work is capable of detecting pipeline leaks with high accuracy.

Application of a novel machine learning model for the prediction and optimization of anti-blast performance of sandwich honeycomb blast walls

Sandwich honeycomb blast wall with the superior energy absorption characteristics could be applied to anti-blast protection of offshore oil and gas platform. Recently, machine learning technique, as an alternative to traditional Finite Element Analysis (FEA), is becoming a powerful tool in understanding structural performance. The aim of this study is to propose a novel data-driven model for fast predictions of structure response of sandwich blast wall under explosion, including the maximum deformation δmax, the maximum internal energy Emax and the energy absorption ratio RE. FEA was firstly conducted to generate sample data for the proposed model by using LS-DYNA software. An improved artificial electric field algorithm (IAEFA) was combined with artificial neural network (ANN) to develop an IAEFA-ANN model. In case studies, the prediction results of IAEFA-ANN approximate the results of FEA well, and comparison results of 9 different ANN models demonstrate that IAEFA-ANN model has the best performance. Then, the effects of explosion parameters (including explosive charge mass Me, stand-off distance d and concave angles θ) on structural response prediction results were investigated by using the proposed model. Moreover, the IAEFA-ANN model was applied to conducting optimal design of the blast wall to obtain optimum anti-blast performance.

VFD-based Coordinated Centralized/decentralized Control to Support Offshore Electrical Power Systems with Wind Power Penetration

This paper proposes a coordinated centralized/decentralized control designed to concomitantly manage the power factor and mitigate voltage variations at the main feeder of an oil and gas offshore platform. The centralized action (i.e., power factor control) is decoupled from the decentralized one (i.e., voltage support) due to the significant disparity in dynamics of the commands sent to the active front-end variable frequency drives (AFE-VFDs). The AFE-VFDs are embedded with a proposed V-Q inverse droop curve, while being remotely coordinated by a central controller using a low-bandwidth communication link. Unlike other strategies, the proposed control is effective in handling unplanned events and unpredictable disturbances. Simulations are conducted to compare the proposed strategy with a literature solution considering a typical Brazilian floating production storage and offloading platform, from Mero Oil Field.

Structure size may affect fish density around oil platforms

Abstract Thousands of offshore oil and gas platforms have been installed worldwide and are known to act as artificial reefs. Many platforms are nearing the end of their operational lives and will soon require decommissioning, but uncertainty remains about the impacts of these structures, and their removal, on the environment. Fish aggregate at platforms, but little is known about the extent of these effects in the North Sea and the causes of variability in these associations. Here, an uncrewed surface vessel (USV) was used to collect fisheries acoustic data on distributions of schooling and non-schooling fish around six oil platforms, collecting data within tens of metres of four of the surveyed platforms. In areas with more platforms, more non-schooling fish were found, and the probability of detecting fish schools was higher. Interplatform variability was found in trends in non-schooling fish density with increasing distance from platform, but the relationship was found to be strongest and most negative at the larger platforms. These findings may influence future management decisions around the decommissioning of these platforms, particularly if some structure is to be left in place to maximize the potential benefits associated with these artificial reef effects.

Influence of graphene oxide additives on the NF separation of triazine-based H2S scavenging compounds using advanced membrane technology

This work proposes an innovative approach for the membrane separation of spent and unspent H2S scavengers (SUS) derived from the application of MEA-triazine in offshore oil and gas production. Modified nanofiltration membranes were fabricated by incorporating graphene oxide (GO) and polyvinyl alcohol (PVA) into a thin film composite (TFC) to obtain a thin film nanocomposite (TFN) with enhanced permeability. In addition, various immobilization strategies for GO were investigated. The performance of the membranes and the effect of the GO loading were evaluated in terms of permeability, fouling propensity, and rejection of key components of the SUS, i.e., MEA-triazine (unspent scavenger), dithiazine (spent scavenger), and monoethanolamine, operating on a sample of SUS wastewater obtained from an offshore oil and gas platform. Various characterization techniques, such as contact angle, FTIR, XRD, SEM, TGA, and AFM, were employed to evaluate the structure, composition, and hydrophilicity of the membrane. The results show a remarkable increase in permeability (from 0.22 Lm-2 h−1 bar−1 for the TFC to 5.8 Lm-2 h−1 bar−1 for the TFN membranes), due to the enhanced hydrophilicity from GO incorporation. The strong interfacial interaction between GO and PVA within the TFN membrane results in negligible nanofiller leaching. The incorporation of GO moderately increases the rejection of the unspent scavenger (63%–73%, 62%–79%, 62%–80%, and 68%–76%), while drastically increasing the rejection of the spent scavenger, which is approximately null for the TFC membrane without GO and increases up to 58% in the TFN membrane with GO. Therefore, while the proposed membranes cannot be used for the selective separation of the unspent form the spent scavenger, they can achieve substantial recovery of all the key components contained in the SUS to avoid their discharge into the sea.

Marine Infrastructure Detection with Satellite Data—A Review

A rapid development of marine infrastructures can be observed along the global coasts. Offshore wind farms, oil and gas platforms, artificial islands, aquaculture, and more, are being constructed without a proper quantification of these human activities. Therefore, effective monitoring is required to maintain transparency towards environmental standards, marine resource management, inventorying objects, and global security. This study reviews remote sensing-based approaches to offshore infrastructure detection over the past 12 years. We analyzed 89 studies from over 30 scientific journals, highlighting spatial and temporal trends, methodological approaches, and regional and thematic research foci. Our results show a significant increase in research interest, especially since 2019. Asia, and especially China, is the predominant focus region in terms of first authorship, funding, and areas of investigation. Aquaculture is the most studied infrastructure, followed by platforms, offshore wind farms, and artificial islands. Gaofen, Sentinel, and Landsat are the most used satellite sensors for detection. The apparent shift towards automated detection methods, especially Deep Learning algorithms, reflects advances in computer vision. This study highlights the key role of earth observation in the field of off-shore infrastructure detection, which can contribute towards outlining effective monitoring practices for marine activities, as well as highlighting important knowledge gaps.

Elevated fish densities extend kilometres from oil and gas platforms.

Thousands of offshore oil and gas platforms have been installed throughout the world's oceans and more structures are being installed as part of the transition to renewable energy. These structures increase the availability of ecological niches by providing hard substrate in midwater and complex 3D habitat on the seafloor. This can lead to 'hotspots' of biodiversity, or increased densities of flora and fauna, which potentially spill over into the local area. However, the distances over which these higher densities extend (the 'range of influence') can be highly variable. Fish aggregate at such structures, but the range of influence and any implications for wider fish populations, are unclear. We investigated the relationship between fish and platform areal densities using high resolution fisheries acoustic data. Data were collected in the waters surrounding the vessel exclusions zones around 16 oil and gas platforms in the North Sea, and throughout the wider area. We estimated densities of schooling fish using echo-integration, and densities of non-schooling fish using echo-counting. At 10 platforms, non-schooling fish densities were elevated near the platform relative to background levels in the equivalent wider area. The range of influence, defined here as the range to which fish densities were elevated above background, varied from 0.8 to 23 km. In areas of high platform density, fish schools were encountered more often, and non-schooling fish densities were higher, when controlling for other sources of environmental variation. This is the first time such long-range effects have been identified; previously, ranges of influence have been reported in the order of just 10s-100s of metres. These findings suggest that the environmental impact of these structures may extend further than previously thought, which may be relevant in the context of upcoming management decisions around the decommissioning of these structures.

Prediction Model for Trends in Submarine Cable Burial Depth Variation Considering Dynamic Thermal Resistance Characteristics

Fault problems associated with submarine cables caused by variations in their burial depth are becoming increasingly prominent. To address the difficulty of detecting the burial depth of submarine cables and trends in its variation, a prediction model for submarine cable burial depth was proposed which considers the dynamic characteristics of thermal resistance. First, a parallel thermal circuit model of a three-core submarine cable was established, and a formula for calculating the submarine cable’s burial depth was derived based on a formula for calculating the submarine cable’s core temperature. Then, the calculation result was corrected by considering the dynamic characteristics of the thermal resistance of the submarine cable’s structural materials. On this basis, feature vectors associated with the seabed cable burial depth calculation data and time nodes were mined by a convolutional neural network and used as the input parameters of a long short-term memory network for optimization and training, and a prediction model for trends in seabed cable burial depth variation was obtained. Finally, an example analysis was carried out based on the actual electrical parameter data of submarine cables buried by an offshore oil and gas platform. The results showed that the prediction model for trends in variations in the burial depth of submarine cables based on the CNN-LSTM neural network can achieve high prediction accuracy and prediction efficiency.

Haven or hell? A perspective on the ecology of offshore oil and gas platforms

Offshore oil and gas platforms (OGP) have been installed worldwide and initially with limited consideration given to the nature of their positive or negative long-term interactions with the natural marine habitats. However, as OGP reach the end of their useful life, with many being decommissioned and removed, it is timely to review the growing evidence of the association of marine biota with OGP to provide a summary and synthesis for policy makers and to give insight to decisions in increasingly crowded marine spatial plans. In the last decade, there has been rapid increase in studies concerning the ecological role of OGP. This research reveals strong contextual difference between platforms in different geographical regions, but all OGP add to local biodiversity particularly where hard substrata are introduced to areas dominated by depositional (mud and sand) habitats. This includes the attraction and increased productivity of fish, sessile invertebrates, and algae while also affecting change in the benthic habitats beneath platforms. There also evidence of the OGP changing local hydrodynamics conditions with effects on phytoplankton and local scour. In terms of the biota associated with OGP, water depth is a major driver of community type across systems. This study emphasises that while knowledge of OGP communities and species has improved, there are still significant knowledge gaps that may prevent the most environmentally beneficial decisions being made around decommissioning. There are few studies following the effect of decommissioning (topping, toppling, or removal) on the ecology of the systems as they change with time (longitudinal research) for the decommissioning event. There is also a need for more studies comparing the biodiversity and functionality of OGP system to artificial and natural reefs and habitats to better understand the ecological cost-benefit of decommissioning scenarios. Finally, commercial data is often unavailable and even when available, surveys are often conducted using varied methodology that prevents comparative analysis. By imposing/agreeing standards and sharing data around the ecological cost-benefit of decommissioning strategies, improve policy guidance concerning OGP planning, and management might emerge.

Contribution of offshore platforms and surrounding habitats to fish production in the Bass Strait, south-east Australia

Information on the contribution of offshore oil and gas (O&G) platforms to fish productivity is required to contribute to the decision-making process to remove, partially remove, or retain these structures during decommissioning after petroleum production ceases. The present study assesses the biomass and fish production of one common and abundant fish species (Caesioperca lepidoptera – butterfly perch) and two commercially fished species (Helicolenus percoides – reef ocean perch; Nemadactylus macropterus – jackass morwong) on eight O&G platforms and in surrounding natural habitats in the Bass Strait, south-east Australia, where options for decommissioning are being assessed. High-definition stereo-video imagery was collected by remotely operated vehicle (ROV) from eight platform facilities, their immediate benthic surrounds, reference areas reflective of the likely pre-installation seabed state (sand-dominated) and a nearby natural ‘reef’ area referred to as south-east reef (some limestone foundation). The biomass of all three species was low in the benthic surrounds of platforms, at reference locations and at south-east reef where minimal cover by benthic organisms was recorded and, as such, there was little to no fish production for the three study species in these areas. We observed a total fish biomass of 2.85 tonnes across the eight platforms for the three fish species surveyed, with high variability across platforms. Total production (P) across all platforms was estimated at 1244 kg/year for the three species, with a mean fish production density of 82 g/m2/year. Approximately 79% of total production is considered ‘new’ production (984 kg/year i.e., the production attributed to the presence of the platforms; with a mean production density of 64 g/m2/year). The remaining 21% could be retained or redistributed into the surrounding area if platforms were removed. C. lepidoptera accounted for the majority (90%) of biomass and of total production for all three species across all locations surveyed. Despite only accounting for a small proportion of platform surface area, the bottom 5 m sections of platforms had 41% of the total biomass observed and accounted for 46% of total production of these three study species. Production measures for platforms surveyed here are relatively high compared to other artificial reefs and habitats around the world. Total removal of these platforms will likely result in a reduction of fish biomass and fish productivity (incl. several fishery species) in the immediate vicinity.

Combined Filtering Method for Offshore Oil and Gas Platform Point Cloud Data Based on KNN_PCF and Hy_WHF and Its Application in 3D Reconstruction.

With the increasing scale of deep-sea oil exploration and drilling platforms, the assessment, maintenance, and optimization of marine structures have become crucial. Traditional detection and manual measurement methods are inadequate for meeting these demands, but three-dimensional laser scanning technology offers a promising solution. However, the complexity of the marine environment, including waves and wind, often leads to problematic point cloud data characterized by noise points and redundancy. To address this challenge, this paper proposes a method that combines K-Nearest-Neighborhood filtering with a hyperbolic function-based weighted hybrid filtering. The experimental results demonstrate the exceptional performance of the algorithm in processing point cloud data from offshore oil and gas platforms. The method improves noise point filtering efficiency by approximately 11% and decreases the total error by 0.6 percentage points compared to existing technologies. Not only does this method accurately process anomalies in high-density areas-it also removes noise while preserving important details. Furthermore, the research method presented in this paper is particularly suited for processing large point cloud data in complex marine environments. It enhances data accuracy and optimizes the three-dimensional reconstruction of offshore oil and gas platforms, providing reliable dimensional information for land-based prefabrication of these platforms.

Performance analysis of ocean thermal energy conversion system integrated with waste heat recovery from offshore oil and gas platform

To improve ocean thermal energy conversion (OTEC) system efficiency, four systems utilizing the waste heat recovery from offshore oil and gas platform are proposed, including flue gas boosting OTEC (system I), production water boosting surface seawater OTEC (system II), production water boosting working medium OTEC (system III), and production water boosting vapor OTEC (system IV). The system thermodynamic performance are investigated. The results show that system IV has larger power generation (Wnet), thermal efficiency (ηth) and exergy efficiency (ηex) than others. Compared with single OTEC system, for system IV, Wnet, ηth and ηex are increased by 1569.13 %, 70.35 % and 138.26 %, respectively. For system IV, with the increase of flue gas waste heat quantity from 2000 to 4000 kW, Wnet, ηth and ηex are increased by 562 %, 390 % and 181 % respectively; with the increase of production water waste heat quantity from 0 to 100 kW, Wnet, ηth and ηex are 12.59 %, 5.73 % and 2.86 % respectively. Wnet rises first and then decreases with the increase of evaporation pressure (Peva) or base fluid concentration (xb), presenting an optimal Peva of 1.5 MPa and xb of 0.82 corresponding to the maximum Wnet; Wnet decreases with the increase of condensation temperature.

Public support and opposition toward floating offshore wind power development in Norway

For countries like Norway, with abundant offshore wind resources and deep seas, floating wind power technology can play an essential role in the green energy transition. However, this technology is still immature, and the first utility-scale floating offshore wind power projects need substantial support for technology development to be commercially feasible. This study employs an online survey targeting the general population in Norway (N = 1011) to investigate support and opposition toward floating offshore wind power development. The survey includes a discrete choice experiment focusing on policy-relevant factors such as the export of electricity, reducing domestic carbon emissions by electrifying offshore oil and gas platforms, impact on global technology cost trends, and involving domestic offshore industries as key players in the floating offshore wind sector. We find the highest support for developing projects that utilize technology from domestic offshore industries and projects connected to the domestic electricity grid. Projects aimed at reducing domestic carbon emissions by electrifying offshore oil and gas platforms are favored over those for exporting electricity to other countries. A significant impact on future technology costs does not lead to increased support for the project. Projects presented after a framing text focusing on meeting future electricity demand result in a higher willingness to pay for floating offshore wind projects than those presented after a framing text focusing on meeting climate objectives. Respondents opposing all the projects are likely climate skeptics and believe that project developers should bear all the project costs. Norway is expected to play a critical role in developing floating wind power. However, the Norwegians demand clear national benefits to be willing to shoulder the cost of spearheading the floating offshore wind power development. Understanding these preferences is vital for crafting energy policies aligning with public interests and rapidly integrating floating wind power into the green energy transition.

Proposal for Low-Carbon Transformation of Decommissioned Offshore Oil and Gas Platforms in China

Proposal for Low-Carbon Transformation of Decommissioned Offshore Oil and Gas Platforms in China

Enhancing Sustainability and Efficiency in Offshore Oil and Gas Engineering through the Integration of Chaotic Local Search and Particle Swarm Optimization for Microenergy Systems Optimization

The offshore oil and gas industry is under increasing pressure to reduce carbon emissions while maintaining energy reliability. Offshore oil and gas platforms (OOGPs) face significant challenges in integrating low‐carbon operations with their energy systems. This study introduces an optimized scheduling approach for offshore microintegrated energy system (OMIES) that incorporates a hybrid energy storage system, including a floating power‐to‐gas associated gas storage (FP2G‐AGS) module, to address the intermittency of renewable energy sources. An economic optimization model is formulated, accounting for carbon emissions, operational costs, and the status of gas turbine generator sets. To solve the complex optimization problem, this study develops a hybrid chaotic local search and particle swarm optimization (CLPSO) algorithm. The CLPSO algorithm synergizes the global search ability of PSO with the local refinement of chaotic local search, enhancing the convergence to optimal solutions. Experimental results demonstrate that the proposed CLPSO algorithm effectively achieves optimal solutions within a range of 48.2–51.7. Case studies validate the model’s capability to promote new energy integration, reduce operational costs, and decrease CO2 emissions across various scenarios. This research significantly contributes to achieving low‐carbon operations on OOGPs and promotes the sustainable development of marine resources.

Optimal Energy Management in Autonomous Power Systems With Probabilistic Security Constraints and Adaptive Frequency Control

The decarbonization of many heavy power-consuming industries is dependent on the integration of renewable energy sources and energy storage systems in isolated autonomous power systems. The optimal energy management in such schemes becomes harder due to the increased complexity and stability requirements, the rapidly varying operating conditions and uncertainty of renewable sources, the conflicting objectives across different timescales, the limited amount of reliable power sources and energy storage. The state of charge management when energy storage is used for multiple services, such as optimal scheduling and frequency support, is one of the most notorious problems in this context. To address this issue, an optimal energy management system is proposed in this paper. It co-optimizes the primary frequency control layer and the dispatch schedule of conventional generators and energy storage by taking advantage of an algorithm that provides adaptive active power demand uncertainty quantification, theoretical guarantees for frequency stability, and bounds for the reserves for frequency support assigned to the energy storage system. A pattern-based reformulation of the frequency stability constraints is derived enabling the efficient solution of the involved optimization problem, being a test case of an isolated offshore oil and gas platform presented for validation.

An empirical model to predict elongation of polyamide mooring lines

Polyamide mooring lines are being proposed for station-keeping of floating wind turbines in shallow water. However, their mechanical behaviour is quite different to that of the polyester fibre ropes used for deep water offshore oil & gas platform moorings. This paper presents an empirical model which can predict the specific strain response of a polyamide 6 mooring line under stochastic loading. Particular attention is given to the non-linear load-strain behaviour, hysteresis, and the influence of loading history. The identification of the model, using 12 stochastic tests performed on wet 3-strand polyamide sub-rope samples, is described. This model reproduces the specific features of the response of fully wetted PA ropes well, with reasonable accuracy and, for the extreme (maximum and minimum) values it provides a much better prediction than the linear “dynamic stiffness” model. This new model can be integrated in the time domain processing of a suitable mooring analysis software.

Experimental study on pull-out behaviour of piles in cohesive sedimentary beds subject to water jet and vibration loading

With the development of offshore oil and gas platforms and the continuous promotion of offshore wind farms, offshore pile decommissioning will become an important issue. However, the complete removal of offshore piles is complex and significant. The difficulty in removing offshore piles embedded in a cohesive sedimentary bed is usually related to the properties of the cohesive sediments. In this study, we investigated the problem of pile pullout from cohesive sediments under the combined action of a water jet and vibration loading. Through a series of pile pull-out tests, we studied the effects of pile diameter, soil strength, water jet flow rate, vibration intensity, and vertical pull-out speed on the maximum pull-out force of the pile. By combining force and dimensional analyses with test data, we proposed a formula for calculating the maximum pull-out force of a pile embedded in a cohesive sedimentary bed and verified by other researchers.

Comparative study of two cost assessment approaches to models for offshore oil and gas platform decommissioning

Offshore oil and gas facilities have been developed for over a century. The current exploration, development, and mining technologies are already perfect. However, the offshore oil and gas facilities in the world's major offshore oil-producing countries face serious aging challenges and many are overdue for service. Moreover, many oil and gas platforms are in urgent need of decommissioning. The cost of decommissioning is one of the most concerning issues for energy companies and governments. This study compared two different approaches to cost assessment models built by using United Kingdom Continental Shelf (UKCS) decommissioned platforms: a model constructed using a popular top-down approach, and one created by a novel bottom-up approach. Both models use the completed decommissioning projects in the UKCS area as research and verification resources. According to the results, although the cost assessment model constructed using the bottom-up method is challenging, it has higher accuracy and more robust versatility. The top-down approach model is more suitable for governments and energy companies to make rough assessments of the market and projects. The bottom-up approach model is more suitable for a more detailed cost assessment of the decommissioning of individual facilities and can be integrated with results from other assessment models (such as risk and impact assessment models) to obtain more accurate cost assessment results. Both approaches have great room for improvement at present, and further integration of disciplines may be the key to breakthroughs in this field.