Offshore Oil 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.

AI-powered pandemic response framework for offshore oil platforms: Ensuring safety during global health crises

AI-powered pandemic response framework for offshore oil platforms: Ensuring safety during global health crises

Development of a Meander-Coil-Type Dual Magnetic Group Circumferential Magnetostrictive Guided Wave Transducer for Detecting Small Defects Hidden behind Support Structures.

In order to solve the problem that small defects hidden behind pipeline support parts are difficult to detect effectively in small spaces, such as offshore oil platforms, a meander-coil-type dual magnetic group circumferential magnetostrictive guided wave transducer is developed in this paper. The transducer, which consists of a coil, two sets of permanent magnets, and a magnetostrictive patch, can excite a high-frequency circumferential shear horizontal (CSH) guided wave. The energy conversion efficiency of the MPT is optimized through magnetic field simulation and experiment, and the amplitude of the defect signal is enhanced 1.9 times. The experimental results show that the MPT developed in this paper can effectively excite and receive CSH2 mode guided waves with a center frequency of 1.6 MHz. Compared with the traditional PPM EMAT transducer, the excitation energy of the transducer is significantly enhanced, and the defects of the 2 mm round hole at the back of the support can be effectively detected.

Failure investigation of an AISI 316L pipe of the flare system in an off-shore oil platform

The oil and gas industries have severe operations conditions, which impose several failures in off-shore equipment. The Flare System is a pipe designed to safely dispose of relieving hydrocarbon gases and liquids during start-up, operational upsets, emergency shutdown, and maintenance activities. This work reported the failure investigation in a pipe of a high-pressure flare system. The pipe has a diameter of 220 mm and 4 mm of thickness, and according to the equipment datasheet, it was made in AISI 316L stainless steel. Two cracks were detected during the previous non-destructive inspection. Those failures were repaired, but another crack was detected after some months. Chemical composition was investigated using optical emission spectroscopy and elemental analysis, and the results are compatible with AISI 316L steel. The microstructure observed by light optical and scanning electron microscopes shows an austenitic matrix with deformation bands. The microhardness measurements and the XRD analysis show the martensite formation in those deformation bands. The tensile properties are in accordance with the standards. The fracture surface was investigated using scanning electron microscopy, the results point to fatigue failure.

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.

The long mid-transition: tracking decades of coastal oil well decommissioning in California

Abstract This article presents an account of four different experiences of oil well decommissioning and abandonment along the Santa Barbara coast in California, together forming an ongoing process that began over a century ago and which has, more recently, become a component of statewide transition plans. The cases include the re-abandonment of nineteenth century wells in Summerland; offshore oil platform removal in the 1990s; and two different areas of operational closures due to infrastructure failures and bankruptcies. Informed by this history, we suggest that decommissioning must be actively managed through coordinated, well-funded, multi-agency action, grounded in historical awareness of policy trajectories. Additionally, the scale of the need together with the economics and policies in place mean that oil well decommissioning will be happening for a very long time, even if no new wells are built.

Determining Marine Biodegradation Kinetics of Chemicals Discharged from Offshore Oil Platforms─Whole Mixture Testing at High Dilutions Increases Environmental Relevance.

Offshore oil platforms discharge enormous volumes of produced water that contain mixtures of petrochemicals and production chemicals. It is crucial to avoid the discharge of particularly those chemicals that are persistent in the marine environment. This study aims to (1) develop a biodegradation testing approach for discharged chemicals by native marine microorganism, (2) determine how dilution affects biodegradation, and (3) determine biodegradation kinetics for many discharged chemicals at low and noninhibitory concentrations. Produced water from an offshore oil platform was diluted in the ratio of 1:20, 1:60, and 1:200 in seawater from the same location and incubated for 60 days at 10 °C. Automated solid-phase microextraction GC-MS was used as a sensitive analytical technique, and chemical-specific primary degradation was determined based on peak area ratios between biotic test systems and abiotic controls. Biodegradation was inhibited at lower dilutions, consistent with ecotoxicity tests. Biodegradation kinetics were determined at the highest dilution for 139 chemicals (43 tentatively identified), and 6 chemicals were found persistent (half-life >60 days). Nontargeted analysis by liquid chromatography-high-resolution MS was demonstrated as a proof-of-principle for a comprehensive assessment. Biodegradation testing of chemicals in discharges provides the possibility to assess hundreds of chemicals at once and find the persistent ones.

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.

A reconstruction method of stress fields, bending moments and wave parameters for floating nuclear power plants through regular wave identification

Floating nuclear power plants (FNPPs) have the potential to supply abundant electricity, hot water, and freshwater resources for offshore islands and offshore oil and gas extraction platforms. However, the consequences of a nuclear reactor accident can be extremely serious. In addition, there may be many uncertain factors in the actual operation of FNPPs. These factors lead to higher structural safety requirements. So， developing more scientific structural monitoring methods is an effective way to enhance the structural safety of FNPP. Traditional hull monitoring schemes can only track bending and torsional forces on specific cross-sections, failing to identify the overall stress field of the entire vessel structure. The paper proposes a reconstruction method for the stress field, bending moment distribution, and wave parameters of FNPP using regular wave identification. Initially, irregular waves are decomposed into a linear combination of multiple regular waves with different amplitudes and frequencies. Subsequently, the strains induced by these regular waves on the FNPP are utilized as basic functions to establish a mathematical model for reconstructing the stress field. The stress field, bending moment distribution, and wave parameters of the FNPP can be reconstructed by identifying the combination coefficients of the regular wave loads. The ill-posedness of the mathematical model for stress field reconstruction has the potential to significantly impact the accuracy of the stress field reconstruction, which was addressed by optimizing the sensor installation position in this study. Meanwhile, the optimization of the sensor installation position greatly reduces the dimensionality of mathematical models. Finally, a numerical study, i.e. FNPP being designed by China, was used to validate the accuracy of the proposed stress field reconstruction algorithm. The reconstructed stress field of the FNPP closely matched the original stress distribution under various irregular wave conditions.

Thermodynamic and life cycle assessment analysis of polymer-containing oily sludge supercritical water gasification system combined with Organic Rankine Cycle

With the development of polymer flooding technology, polymer-containing oily sludge (PCOS) has become a major pollutant on offshore oil platforms. Supercritical water gasification system is an efficient and pollution-free technology compared to conventional PCOS treatment. However, the system model for PCOS has not yet been established and the theoretical model for system optimization is insufficient. In this work, a novel auto-thermal system is established based on supercritical water gasification technology for the treatment of PCOS. Through the thermodynamic analysis, an optimization plan is concluded and used for the establishment of a modified system with Organic Rankine Cycle. Results show that the two-stage reactor reduces the gasification reaction temperature from 652 °C to 502 °C, and the Organic Rankine Cycle system using Isopropanol recover 9.1 % chemical exergy of feedstock with the optimal parameters (superheat, evaporation temperature and circulating pressure). The energy and exergy efficiency of the modified system has increased by 52.4 % and 42.4 % compared to the original system. Besides, a life cycle assessment is adopted for the environment analysis. The results show that the increasing gasification temperature and PCOS slurry concentration can reduce system unit exergy loss. Global Warming Potential reaching the minimum value of 26.1 at 65 wt%.

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.

Assessing the potential of nematode metabarcoding for benthic monitoring of offshore oil platforms

Environmental DNA metabarcoding is gaining momentum as a time and cost-effective tool for biomonitoring and environmental impact assessment. Yet, its use as a replacement for the conventional marine benthic monitoring based on morphological analysis of macrofauna is still challenging. Here we propose to study the meiofauna, which is much better represented in sediment DNA samples. We focus on nematodes, which are the most numerous and diverse group of meiofauna. Our aim is to assess the potential of nematode metabarcoding to monitor impacts associated with offshore oil platform activities. To achieve this goal, we used nematode-optimized marker (18S V1V2-Nema) and universal eukaryotic marker (18S V9) region to analyse 252 sediment DNA samples collected near three offshore oil platforms in the North Sea. For both markers, we analysed changes in alpha and beta diversity in relation to distance from the platforms and environmental variables. We also defined three impact classes based on selected environmental variables that are associated with oil extraction activities and used random forest classifiers to compare the predictive performance of both datasets. Our results show that alpha- and beta-diversity of nematodes varies with the increasing distance from the platforms. The variables directly related to platform activity, such as Ba and THC, strongly influence the nematode community. The nematode metabarcoding data provide more robust predictive models than eukaryotic data. Furthermore, the nematode community appears more stable in time and space, as illustrated by the overlap of nematode datasets obtained from the same platform three years apart. A significative negative correlation between distance and Shannon diversity also advocates for higher performance of the V1V2-Nema over the V9. Overall, these results suggest that the sensitivity of nematodes is higher compared to the eukaryotic community. Hence, nematode metabarcoding has the potential to become an effective tool for benthic monitoring in marine environment.

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.

High Impedance Arc Fault Modelling in Offshore Oil Platform Power Grid

High Impedance Arc Fault Modelling in Offshore Oil Platform Power Grid

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.

The vertical distribution of fish on two offshore oil platforms

Knowledge of platform ecology is necessary to best inform decommissioning practice. Remotely operated vehicle (ROV) video is often collected during standard industry operations and may provide insight into the marine life associating with offshore platforms, however, the utility of this video for ecological assessments remains unclear. Archival ROV video surveys at the Wandoo oil platforms on Australia’s North West Shelf was evaluated for its utility, with only 4.9% of imagery usable for standardised ecological studies. Based on the subset of usable ROV video, the influence of depth and structural complexity on attributes of the fish assemblage on the Wandoo oil platforms was examined. Vertical ROV transects on three vertical shafts on the Wandoo platforms were stratified into 10 m depth strata from 0 to 50 m, with 111 fish taxa from 25 families identified and counted across all depth strata. At both platforms, taxonomic richness and abundance was significantly highest in shallow regions and declined with depth. Small reef fish were predominantly associated with structurally complex habitat in shallow regions (<22 m), whilst large demersal species dominated below 32 m. Future decommissioning policy in Australia should consider the vertical fish distributions and the importance of shallow sections of platforms. Finally, the dearth of usable video was due to the haphazard method of collection and it is recommended that future surveys should be conducted according to scientific standards to ensure greater utility of the video for both industry use and scientific research.