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.

Life on every stone: Characterizing benthic communities from scour protection layers of offshore wind farms in the southern North Sea

The scour protection layer (SPL) is a layer of large stones placed around man-made structures in the marine environment, preventing sediment scouring while also providing new hard substrate and potentially increasing the structural complexity of the original environment. This fosters development of diverse benthic communities, supporting high abundance of organisms. Future SPLs are therefore a potential tool for the ecological enhancement of degrading marine habitats following the principles of nature-inclusive design. Yet, factors that shape the benthic communities on SPLs are poorly understood. Here, we analysed existing data from SPLs from offshore wind farms and a gas platform in the southern North Sea to determine how SPL characteristics affect the biofouling community structure. We combined this analysis with an in-situ experiment testing for the effects of habitat complexity on SPL communities. Our results demonstrate that abundant and diverse communities are present on all SPLs. On a regional scale, communities are mainly affected by depth and location. Increasing habitat complexity has significant and positive effects on species richness yet was non-significant for biomass and abundance of the biofouling community. If applied thoughtfully, nature-inclusive design of the SPL habitat, including manipulation of the physical complexity of the structure, can effectively promote biodiversity.

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.

Association of reef fish with oil and gas platforms in the Northern Gulf of Mexico

Association of reef fish with oil and gas platforms in the Northern Gulf of Mexico

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.

Bioconcentration and lethal effects of gas-condensate and crude oil on nearshore copepod assemblages

The progressive establishment of gas platforms and increasing petroleum accidents pose a threat to zooplankton communities and thus to pelagic ecosystems. This study is the first to compare the impacts of gas-condensate and crude oil on copepod assemblages. We conducted microcosm experiments simulating slick scenarios at five different concentrations of gas-condensate and crude oil to determine and compare their lethal effects and the bioconcentration of low molecular weight polycyclic aromatic hydrocarbons (LMW-PAHs) in eastern Mediterranean coastal copepod assemblages. We found that gas-condensate had a two-times higher toxic effect than crude oil, significantly reducing copepod survival with increased exposure levels. The LMW-PAHs bioconcentration factor was 1–2 orders of magnitude higher in copepods exposed to gas-condensate than in those exposed to crude oil. The median lethal concentration (LC50) was significantly lower in calanoids vs. cyclopoid copepods, suggesting that calanoids are more susceptible to gas-condensate and crude oil pollution, with potential trophic implications.

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.

Oil and gas platforms degrade benthic invertebrate diversity and food web structure

Oil and gas exploitation introduces toxic contaminants such as hydrocarbons and heavy metals to the surrounding sediment, resulting in deleterious impacts on marine benthic communities. This study combines benthic monitoring data over a 30-year period in the North Sea with dietary information on >1400 taxa to quantify the effects of active oil and gas platforms on benthic food webs using a multiple before-after control-impact experiment. Contamination from oil and gas platforms caused declines in benthic food web complexity, community abundance, and biodiversity. Fewer trophic interactions and increased connectance indicated that the community became dominated by generalists adapting to alternative resources, leading to simpler but more connected food webs in contaminated environments. Decreased mean body mass, shorter food chains, and the dominance of small detritivores such as Capitella capitata near to structures suggested a disproportionate loss of larger organisms from higher trophic levels. These patterns were associated with concentrations of hydrocarbons and heavy metals that exceed OSPAR's guideline thresholds of sediment toxicity. This study provides new evidence to better quantify and manage the environmental consequences of oil and gas exploitation at sea.

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.

Automated machine learning driven model for predicting platform supply vessel freight market

Platform Supply Vessels (PSVs) play an essential role in supporting oil and gas platforms, and other offshore structures by transporting crew members, personnel, provisions, and further indispensable equipment from onshore to operational sites. PSV freight rate movements are subject to a complex array of non-linear interconnected influential factors. Reviewed literature reveals an absence of forecasting studies predicting PSV freight rates. Meanwhile, Automated Machine Learning (AutoML) frameworks have never before been employed to forecast maritime freight rates. Therefore, this study investigates factors influencing PSV time charter freight rates and explores AutoML modelling in capturing non-linearities while forecasting PSV freight rates over a 1, 3 and 6-month out-of-sample forecast horizon. A total of 43 relevant factors are included in prediction modelling, the most comprehensive number of explanatory variables in the shipping forecasting literature to date. The data consists of 188 monthly observations collected from two databases: Clarksons Shipping Intelligence Network and Offshore Intelligence Network. Time-lagged variables are utilized as data of explanatory variables are not immediately available at the time of forecasting. A total of 79 complex machine learning models are tested, and the best-performing models are Eureqa Generalized Additive Model, eXtreme Gradient Boosted Trees Regressor, and Ridge Regressor with Forecast Distance Modelling, benchmarked against the proven statistical forecasting model triple exponential smoothing. The most influential factors are historical PSV time charter freight rates, newbuilding prices, number of vessel deliveries, orderbook number, total vessel sales, and the unique variable number of active drilling rigs in the market.

REVIEW OF MODERN BATHYMETRIC SURVEY TECHNIQUES AND THEIR IMPACT ON OFFSHORE ENERGY DEVELOPMENT

This paper presents a comprehensive review of modern bathymetric survey techniques and their impact on offshore energy development. Bathymetric surveys play a crucial role in mapping the seafloor and are essential for various offshore activities, including oil and gas exploration, renewable energy development, and submarine cable installation. The review synthesizes recent advancements in bathymetric surveying technologies, focusing on their role in improving the exploration and monitoring of offshore energy resources. The first section of the paper provides an overview of traditional bathymetric survey methods, such as single-beam and multi-beam echo sounders, which have been widely used for mapping the seafloor. The limitations of these methods, including limited coverage and resolution, are discussed, highlighting the need for more advanced techniques. The second section of the paper discusses the recent advancements in bathymetric survey technologies, such as airborne lidar, satellite-derived bathymetry, and unmanned surface vehicles (USVs). These technologies offer significant improvements in terms of coverage, resolution, and efficiency compared to traditional methods. For example, airborne lidar can provide high-resolution bathymetric data over large areas, making it ideal for rapid seafloor mapping in offshore energy exploration. The third section of the paper examines the impact of these advancements on offshore energy development. Improved bathymetric surveying techniques enable more accurate mapping of the seafloor, which is crucial for identifying suitable locations for offshore energy infrastructure, such as oil and gas platforms, wind farms, and wave energy converters. Furthermore, these techniques facilitate the monitoring of underwater structures and environmental changes, helping to ensure the safe and sustainable operation of offshore energy facilities. In conclusion, modern bathymetric survey techniques have revolutionized offshore energy development by providing more accurate, efficient, and cost-effective methods for mapping the seafloor. These advancements are essential for meeting the growing demand for offshore energy resources while minimizing environmental impact and ensuring operational safety.
 Keywords: Modern Bathymetric, Survey Techniques, Impact, Offshore Energy, Development.

The Politics of Amphibiousness: Shifting Coastal Management in the Netherlands

This paper explores the consequences of a shift in Dutch coastal management. The management approach transitioned from aiming to keep the sea at bay toward the stimulation of dynamic sea-land relations. This shift toward “dynamic management” can be seen as part of wider trends in both ecological and science, technology, and society thinking on coasts as amphibious more-than-human entanglements. We draw on a case study of the Wadden Sea barrier island Ameland to develop the notion of amphibious response-ability. We show that while dynamic management enabled amphibiousness in the land–sea interface, it limited other types of amphibiousness, with consequences for the possibilities to respond to coastal changes. These consequences for amphibious response-abilities became critical when rapid coastal erosion threatened and partially destroyed a gas platform. Our case shows that even when coastal management regimes are amphibious because they unleash and build on natural processes, they can still have harmful consequences, and they can in fact limit the possibilities for integrated responses to coastal change. We conclude by suggesting that heterogeneous knowledge alliances are needed to expose and work with the politics of (amphibious) coastal management regimes.

Integrated supervisory fuzzy control framework for biological wastewater treatment plants operation and their effect on GHG emissions

In this research, the effect on the greenhouse gases (GHG) emissions from biological wastewater treatment plants (WWTP) operation by implementing an integrated supervisory control framework on the bench-mark simulation model 2 gas (BSM2G) platform is studied. Ammonia based aeration control is considered for improving the efficiency of the nitrification process and also nitrate control is considered to enhance the performance of the overall plant. Different control strategies are proposed by considering PI control, Model Predictive control and Fuzzy controllers. A notable change is observed in GHG emissions due to bio-treatment because of the impact of control strategies on N2O emissions during denitrification with respect to the nitration where dissolved oxygen (DO) control alone is considered. In addition, when the nitrate loop is considered, a better change is observed in the overall plant performance. On comparison with other approaches, the proposed control strategies resulted in a reduction in the GHG emissions with marginal effect on the other performance indexes effluent quality index (EQI) and operational cost index (OCI).

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.

Satellite NO2 Trends and Hotspots Over Offshore Oil and Gas Operations in the Gulf of Mexico

AbstractThe Outer Continental Shelf of the Gulf of Mexico (GOM) is populated with numerous oil and natural gas (ONG) platforms which produce NOx (NOx = NO + NO2), a major component of air pollution. The Bureau of Ocean Energy Management (BOEM) is mandated to ensure that the air quality of coastal states is not degraded by these emissions. As part of a NASA‐BOEM collaboration, we conducted a satellite data‐based analysis of nitrogen dioxide (NO2) patterns and trends in the GOM. Data from the OMI and TROPOMI sensors were used to obtain 18+ year records of tropospheric column (TrC) NO2 in three GOM regions: (a) Houston urban area, (b) near shore area off the Louisiana coast, and a (c) deepwater area off the Louisiana coast. The 2004–2022 time series show a decreasing trend for the urban (−0.027 DU/decade) and near shore (−0.0022 DU/decade) areas, and an increasing trend (0.0019 DU/decade) for the deepwater area. MERRA‐2 wind and TROPOMI NO2 data were used to reveal several NO2 hotspots (up to 25% above background values) under calm wind conditions near individual platforms. The NO2 signals from these deepwater platforms and the high density of shallow water platforms closer to shore were confirmed by TrC NO2 anomalies of up to 10%, taking into account the monthly TrC NO2 climatology over the GOM. The results presented in this study establish a baseline for future estimates of emissions from the ONG hotspots and provide a methodology for analyzing NO2 measurements from the new geostationary TEMPO instrument.

Fish aggregations at oil and gas platform foundations in the North Sea

AbstractGlobally, aging offshore oil and gas (O&G) platforms face resource depletion, and regulations often demand platform removal through decommissioning. In the North Sea, >€90 billion will be needed for decommissioning by 2060. However, the influence of O&G platforms on fish communities is poorly understood. This challenges predictions of possible fisheries scenarios associated with different decommissioning options. Here, we examined the role that North Sea platforms played on fish communities. We explored patterns of fish abundance and body size variation along transects from O&G platforms comparing catch per unit effort data. Fish abundance was positively associated with O&G platforms. Atlantic cod, plaice, saithe, and mackerel were most abundant near platforms. Similarly, larger Atlantic cod were near the O&G platforms. Our study provided information for decision‐making regarding platform decommissioning scenarios by evidencing O&G platform habitat provisioning for various commercial fish species.