Seismic Source Parameters Optimization in Shallow Water Area Offshore Abu Dhabi, U.A.E.

At a giant oil-field offshore Abu Dhabi, U.A.E., an ocean-bottom cable (OBC) seismic survey was conducted to acquire seismic data in areas of shallow water and intensely developed production infrastructure. OBC data are acquired utilizing two types of detectors: hydrophones (fluid-pressure change detectors) and single- or multi-component geophones (particle-velocity or acceleration detectors). The conventional seismic processing strategy is to sum the two sensors very early in the processing sequence. However, due to numerous factors, the differences between the physical measurement characteristics of hydrophones and geophones, their data character (including noise levels, multiple content, coupling effects) can be very dissimilar. In this project a strategy was employed which avoided summing of the separate sensor data until just prior to imaging. This allowed us to investigate the differences between the two data types, optimize the processing flow for the individual sensors and then sum the sensor data in a manner which maximized the primary signal content in the dataset. Investigations of the raw field data clearly defined significantly different responses of the separate senor data to the surface wave field. This was attenuated by the utilization of a “physics based” 3D surface wave mitigation algorithm applied to the separate sensor datasets. In addition, the separate sensors clearly demonstrated wavelet character changes beyond what would be predicted from conventional ghost filter modeling. As a result, different wavelet shaping filters and surface consistent amplitude compensation corrections were required to be applied to the two sensor types. In addition, the very small scale ( geophone data, again necessitating separate compensation for this phenomenon. Finally, interpretive driven deconvolution, residual amplitude compensation, attribute validation and zero phasing were applied to the data in order to maximally condition it for subsequent quantitative and qualitative analysis. In this paper, we show a step change in seismic imaging quality on a shallowwater Arabian Gulf dataset as a result of this processing strategy. We will compare our results with those obtained using more conventional approaches.

This paper describes the unique development plan of the M field, a giant offshore field with an areal extent of more than 800 square kilometers (km2), laying under shallow waters northeast of Saudi Arabia. Due to the large number of wells to be drilled, conventional offshore development methods required over 30 offshore platforms to meet the production target efficiently. However, a novel drilling island concept in the form of a causeway was adopted, since the shallow water areas do not allow ocean vessels to set up the jackets and platforms. Utilization of a causeway, combined with the implementation of ultra extended reach drilling (ERD), reduced offshore operations to 11 platforms resulting in enormous savings to the operator. The backbone of the upstream infrastructure is the 20 kilometers (km) long, 11 meter (m) wide main causeway, and 25 drilling islands linked to it through 21 km of lateral causeways. The task of constructing these structures involves movement of more than 40 million cubic meters of sand and rock. The main causeway includes a 2,400 m bridge, in addition to five 150 m and eight 50 m bridges for minimal environmental impact on marine life. Being one of the largest drilling projects to be undertaken, with various reservoir, ERD, and logistical challenges were encountered during the planning and initial execution phases of the project. Meeting the expected production rate, at the lowest development and operating costs, in the safest and most environmental-friendly manner were the main drivers for the current plan. Reservoir heterogeneity, torque and drag, hydraulics, rigs specifications, well intervention, waste management and other specific challenges were overcome successfully using different techniques that are discussed in depth in the paper. Introduction The M field was discovered in 1957 and production began when the C reservoir came on stream in 1964 and the B reservoir was brought on production in 1974. Production from M field is Arabian Heavy with about 27° American Petroleum Institute (API) gravity. The field was shut in during January 1984 due to low demand with less than 1% of the reserves produced. Geological Setting M field is a northwest-southeast trending anticline and measures approximately 28 miles (45 km) in length and 11 miles (18 km) in width. There are six oil-bearing reservoirs in the field: A, B, C, D, E and F/G. The reservoirs in this increment development are the B and C reservoirs, Fig. 1. The C and B reservoirs are primarily limestone with occasional dolomitic intervals and generally exhibit high porosity and permeability. The reservoirs were deposited in a shallow marine carbonate platform capped by tight lime mudstones and algal bounds tone facies. A continuous tar mat, which underlies the oil column in both reservoirs effectively separates the oil column from the aquifer. Development Scenarios A conceptual study was undertaken and evaluated three options for M development in terms of life-cycle costs, safety, and environment. Offshore. This option was the base case and considered 100% offshore development. Due to the shallow water depth, a lot of dredging would have to be done for channels to allow for platform installations, rig movement, and support. In addition, it would also have required more than 30 platforms to develop the field efficiently.

Data play an essential role in asset management decisions. The amount of data is increasing through accumulating historical data records, new measuring devices, and communication technology, notably with the evolution toward smart grids. Consequently, the management of data quantity and quality is becoming even more relevant for asset managers to meet efficiency and reliability requirements for power grids. In this work, we propose an innovative data quality management framework enabling asset managers (i) to quantify the impact of poor data quality, and (ii) to determine the conditions under which an investment in data quality improvement is required. To this end, an algorithm is used to determine the optimal year for component replacement based on three scenarios, a Reference scenario, an Imperfect information scenario, and an Investment in higher data quality scenario. Our results indicate that (i) the impact on the optimal year of replacement is the highest for middle-aged components; (ii) the profitability of investments in data quality improvement depends on various factors, including data quality, and the cost of investment in data quality improvement. Finally, we discuss the implementation of the proposed models to control data quality in practice, while taking into account real-world technological and economic limitations.

Summary The Mariner field is a shallow heavy-oil field located in the UK sector of the North Sea. As part of the development of the field we are investigating which seismic technology is most appropriate for imaging and monitoring this complex field. The main challenge at Mariner is the mapping of the Heimdal sands. The new broadband acquisition solutions all open up new and unique possibilities in broadband seismic data processing, imaging, interpretation and monitoring. Broadband seismic is in its infancy. It is expected that large improvements in seismic processing, imaging/inversion and monitoring will take place in the next years, leading to huge improvements in data quality and more reliable seismic interpretation. In August 2012 a seismic survey using WesternGeco’s multisensor towed-streamer technology was acquired at the Mariner field. Whilst no single interpretation strategy can be applied to all Heimdal sands on any one datatype, it is demonstrated that the multisensor streamer dataset provides a basis for Heimdal sand-mapping that will reduce the reliance on stochastic Heimdal modelling in the drainage strategy. Interpretation products are expected to be associated with less uncertainty than those derived from OBC data.

Shallow water is a complex sound propagation medium, which is affected by the varying spatial–temporal ocean environment. Taking this complexity into account, the classical processing techniques of source localization and environmental inversion may be improved. In this work, a joint tracking approach for the moving source and environmental parameters of the range-dependent and time-evolving environment in shallow water is presented. The tracking scheme treats both the source parameters (e.g., source depth, range, and speed) and the environmental parameters (e.g., water column sound speed profile (SSP) and sediment parameters) at the source location as unknown variables that evolve as the source moves. To counter sample impoverishment and robustly characterize the evolution of the parameters, an improved particle filter (PF), which is an extension of the standard PF, is proposed. Two examples with simulated data in a slowly changing environment and experimental data collected during the ASIAEX experiment are utilized to demonstrate the effectiveness of the joint approach. The results show that we were able to track the source and environmental parameters simultaneously, and the uncertainties were evaluated in the form of time-evolving posterior probability densities (PPDs). The performance comparison confirms that the improved PF is superior to the standard PF, as it can reduce the parameter uncertainties. The tracking capabilities of the improved PF were verified with high accuracy in real-time source localization and well-estimated rapidly varying parameters. Moreover, the influence of different particle numbers on the improved PF tracking performance is also illustrated.

Due to the rapid development of the mobile Internet and the Internet of Things, the volume of generated data keeps growing. The topic of data quality has gained increasing attention recently. Numerous studies have explored various data quality (DQ) problems across several fields, with corresponding effective data-cleaning strategies being researched. This paper begins with a comprehensive and systematic review of studies related to DQ. On the one hand, we classify these DQ-related studies into six types: redundant data, missing data, noisy data, erroneous data, conflicting data, and sparse data. On the other hand, we discuss the corresponding data-cleaning strategies for each DQ type. Secondly, we examine DQ issues and potential solutions for a public bus transportation system, utilizing a real-world traffic big data platform. Finally, we provide two representative examples, noise filtering and filling missing values, to demonstrate the DQ improvement practice. The experimental results show that: (1) The GPS noise filtering solution we proposed surpasses the baseline and achieves an accuracy of 97%; (2) The multi-source data fusion method can achieve a 100% missing repair rate (MRR) for bus arrival and departure. The average relative error (ARE) of bus arrival and departure times at stations is less than 1%, and the correlation coefficient (R) is also close to 1. Our research can offer guidance and lessons for enhancing data governance and quality improvement in the bus transportation system.

Over the last years many data quality initiatives and suggestions report how to improve and sustain data quality. However, almost all data quality projects and suggestions focus on the assessment and one-time quality improvement, especially, suggestions rarely include how to sustain the continuous data quality improvement. Inspired by the work related to variability in supply chains, also known as the Bullwhip effect, this paper aims to suggest how to sustain data quality improvements and investigate the effects of delays in reporting data quality indicators. Furthermore, we propose that a data quality prediction model can be used as one of countermeasures to reduce the Data Quality Bullwhip Effect. Based on a real-world case study, this paper makes an attempt to show how to reduce this effect. Our results indicate that data quality success is a critical practice, and predicting data quality improvements can be used to decrease the variability of the data quality index in a long run.

Abstract. This study addresses the critical need for accurate mapping of submerged terrain, which is essential for hydraulic modeling, environmental monitoring, and water resource management. Traditional bathymetric techniques, such as topographic surveys and acoustic soundings, face spatial continuity and usability challenges in shallow or vegetated waters. Recent advances, including Uncrewed Surface Vessels (USVs) equipped with GNSS and acoustic sensors, along with UAV-based photogrammetry for 3D modeling in clear waters, have expanded capabilities. However, optical methods suffer from depth underestimation due to light refraction, requiring geometric corrections. To address these limitations, the paper proposes a multi-sensor fusion workflow that integrates high-precision topographic data from total stations and GNSS, depth measurements from a USV equipped with a single-beam echo sounder, and UAV-derived optical bathymetry corrected for refraction using Structure from Motion (SfM) techniques. The goal is to combine each method's strengths to overcome their weaknesses and produce an accurate, high-resolution bathymetric model. Validation against ground truth data demonstrated significant improvements in data quality, aligning with standards for shallow-water mapping. Notably, the use of corrected UAV photogrammetry extended effective depth measurements to 4–5 meters, exceeding typical optical limits. The combined methodology ensures robust spatial coverage, precise georeferencing, and transparent independent measurements, making it particularly well-suited for complex lacustrine (lake) environments. The results highlight the operational benefits of using complementary technologies and suggest potential for further enhancement through Machine Learning and Deep Learning techniques to refine data integration and analysis.

Today there are a number of challenges related to construction and operation of engineering structures in water areas, especially in shallow regions. This primarily includes installation of platforms for exploratory and production drilling in sea shelf areas, underwater piping work, and construction of engineering structures. Shallow water areas are the most dynamic zones affected by quick sedimentation and active transportation of upper sediments, which can be a hazard to the already constructed structures (for example, in the form of such phenomena as landslides and sand waves). In addition, high-resolution seismic surveys in water areas allow addressing various tasks related to environmental monitoring and comprehensive water area status tracking. There are four groups of geological engineering tasks addressed by high-resolution seismic surveys: 1) obtaining information about the geological section and geometry of geological structures, 2) obtaining information about area distribution of sediment properties, 3) determining physical properties of sediments, and 4) solving geodynamic tasks. Currently the vast majority of seismic engineering surveys in water areas are carried out using either single-channel (1D) or multi-channel (2D) profiling. At the same time, three-dimensional (3D) exploration systems that offer a number of advantages have been used in petroleum geophysics for quite a while. Nevertheless, such systems are utilized for geological engineering applications only in isolated cases. This is mostly due to the need to solve certain hardware-related and methodological problems during field work and subsequent data processing. Seismic data from the near-surface sections are complicated by typical seismic and geological conditions: small acoustic contrast between sediments under bottom as compared to the water and bottom boundary, high seismic energy absorption in bottom sediments, and considerable alteration of sediment properties with depth. Surveys in shallow water areas bring problems associated with multiple waves in target seismic trace times, re-reflection of useful signals from the bottom and the water-air surface etc.

Preliminary design and dynamic analysis of constant tension mooring system on a 15 MW semi-submersible wind turbine for extreme conditions in shallow water

Theme: Data and Linkage QualityObjectives:
 
 To define health data quality from clinical, data science, and health system perspectives
 To describe some of the international best practices related to quality and how they are being applied to Canada’s administrative health data.
 To compare methods for health data quality assessment and improvement in Canada (automated logical checks, chart quality indicators, reabstraction studies, coding manager perspectives)
 To highlight how data linkage can be used to provide new insights into the quality of original data sources
 To highlight current international initiatives for improving coded data quality including results from current ICD-11 field trials
 
 Dr. Keith Denny: Director of Clinical Data Standards and Quality, Canadian Insititute for Health Information (CIHI), Adjunct Research Professor, Carleton University, Ottawa, ON. He provides leadership for CIHI’s information quality initiatives and for the development and application of clinical classifications and terminology standards.
 Maureen Kelly: Manager of Information Quality at CIHI, Ottawa, ON. She leads CIHI’s corporate quality program that is focused on enhancing the quality of CIHI’s data sources and information products and to fostering CIHI’s quality culture.
 Dr. Cathy Eastwood: Scientific Manager, Associate Director of Alberta SPOR Methods & Development Platform, Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB. She has expertise in clinical data collection, evaluation of local and systemic data quality issues, disease classification coding with ICD-10 and ICD-11.
 Dr. Hude Quan: Professor, Community Health Sciences, Cumming School of Medicine, University of Calgary, Director Alberta SPOR Methods Platform; Co-Chair of Hypertension Canada, Co-Chair of Person to Population Health Collaborative of the Libin Cardiovascular Institute in Calgary, AB. He has expertise in assessing, validating, and linking administrative data sources for conducting data science research including artificial intelligence methods for evaluating and improving data quality.
 Intended Outcomes:“What is quality health data?” The panel of experts will address this common question by discussing how to define high quality health data, and measures being taken to ensure that they are available in Canada. Optimizing the quality of clinical-administrative data, and their use-value, first requires an understanding of the processes used to create the data.
 Subsequently, we can address the limitations in data collection and use these data for diverse applications. Current advances in digital data collection are providing more solutions to improve health data quality at lower cost.
 This panel will describe a number of quality assessment and improvement initiatives aimed at ensuring that health data are fit for a range of secondary uses including data linkage. It will also discuss how the need for the linkage and integration of data sources can influence the views of the data source’s fitness for use.
 CIHI content will include:
 
 Methods for optimizing the value of clinical-administrative data
 CIHI Information Quality Framework
 Reabstraction studies (e.g. physician documentation/coders’ experiences)
 Linkage analytics for data quality
 
 University of Calgary content will include:
 
 Defining/measuring health data quality
 Automated methods for quality assessment and improvement
 ICD-11 features and coding practices
 Electronic health record initiatives

Bottlenose dolphins, Tursiops truncatus, exhibit flexible associations in which the compositions of groups change frequently. We investigated the potential distances over which female dolphins and their dependent calves could remain in acoustic contact. We quantified the propagation of sounds in the frequency range of typical dolphin whistles in shallow water areas and channels of Sarasota Bay, Florida. Our results indicated that detection range was noise limited as opposed to being limited by hearing sensitivity. Sounds were attenuated to a greater extent in areas with seagrass than any other habitat. Estimates of active space of whistles showed that in seagrass shallow water areas, low-frequency whistles (7-13 kHz) with a 165 dB source level could be heard by dolphins at 487 m. In shallow areas with a mud bottom, all whistle frequency components of the same whistle could be heard by dolphins travel up to 2 km. In channels, high-frequency whistles (13-19 kHz) could be detectable potentially over a much longer distance (> 20 km). Our findings indicate that the communication range of social sounds likely exceeds the mean separation distances between females and their calves. Ecological pressures might play an important role in determining the separation distances within communication range.

Satellite image data is being researched and applied effectively in the survey and establishment of bathymetry mapping in shallow water areas in both time and human terms. Remote sensing techniques contribute to rapid updating of topography, timely assurance of civil and military operations such as maritime safety, environmental security and rescue, Warfare in the military, especially the ability to remotely monitor disputed areas. The article experiment with the Stumpf et al algorithm to estimate the shallow water depths on the Spratly Island by Landsat 8 image. The correlation coefficient of the model R2 is 0.924; RMSE is 0.99m. In addition, the results are compared with the map data of C-map and use 12 actual test points scores to evaluate the accuracy of the model.

In the summer of 2021, a seismic survey occurred near Southern Oregon to map the Cascadia Subduction Zone using an array of airguns. To evaluate the effect of the survey on the soundscape within a marine protected area, acoustic measurements were conducted using a vector sensor array deployed in 36 m deep water. Resulting data were analyzed using acoustic metrics, some of which are commonly used in soundscape studies, while others are gaining popularity in ecological acoustics. While cumulative sound exposure levels were dominated by wind noise, other metrics such as peak sound pressure, kurtosis, Acoustic Complexity Index, and crest factor, showed clear signals associated with the seismic survey. Further, when the seismic survey vessel was closest to the measurement location, a good correlation was observed between sound pressure (peak and root mean squared) and sound exposure levels computed using sound pressure versus those computed using particle velocity. At lower signal-to-noise ratios, or in the absence of seismic pulses, velocity-based acoustic metrics showed considerable contamination by flow and other noise. Our analysis suggests that pressure measurements alone may be sufficient (and even preferable) for evaluating organismal particle velocity dosage, whenever the signals of interest have sufficient signal-to-noise ratio.

Presented on 28 May 2025: Session 22 The Bonaparte Basin is well-positioned to support Australia’s Future Gas Strategy, with proven gas-prone petroleum systems ideal for future carbon capture and storage (CCS) and liquefied natural gas (LNG) development. From a drilling perspective, the shallow water depths (70–230 m) present accessible resource opportunities but pose significant challenges for seismic survey operations and subsurface imaging. In early 2024, following a detailed survey design study, a seismic acquisition was carried out using a single vessel towing 12 streamers of 8000-m length with 112.5-m streamer separation, and three acoustic sources at 50-m shotpoint intervals. This survey aimed to improve subsurface imaging of the eastern Vulcan Sub-basin and Londonderry High. Despite challenges – restricted manoeuvrability, carbonate pinnacles, strict environmental controls, plus local and foreign fishers – seismic data were acquired using continuous-line acquisition (CLA) in a racetrack pattern. This approach significantly reduced the survey duration, completing 6808 km2 in just 76 days. Acquiring seismic data with a wide streamer tow and CLA in shallow water demands additional signal processing considerations. The sparse near-offset coverage poses challenges for demultiple, velocity model building, and imaging. To address this, innovative seismic imaging technologies and workflows were deployed. The resulting new seismic image provides improved understanding of the subsurface previously restricted by the quality of legacy seismic which was unable to resolve shallow carbonate intervals and complex faulting. Along with the results, we will also discuss how these technologies could shorten future seismic survey duration and enhance reprocessing of legacy seismic data. To access the Oral Presentation click the link on the right. To read the full paper click here