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Rapid methane reductions are critical to limit global warming in the near term, and more than 150 countries have signed the Global Methane Pledge--a collective agreement to cut methane emissions by 30% by 2030. National policies and significant funding have been announced to support this ambitious initiative, yet there is a lag in credible data to inform and demonstrate progress. The fossil fuel industry, which contributes 15–22% of the global methane budget, is expected to make rapid reductions. Under the International Energy Agency's Net Zero by 2050 scenario, 75% of methane emissions from fossil fuel operations must be eliminated between 2020 and 2030. Global policy approaches to realize these reductions vary.
Xiaogui Miao has extensive experience in land 3D3C and Ocean Bottom Sensor (OBC & OBN) 3D4C imaging from North America to the Asia Pacific (APAC) region. After graduation from the University of Manitoba, Canada with a PhD in Geophysics (1994), Xiaogui Miao joined Veritas Geophysical Services Ltd. in Calgary as a geophysical research scientist. In 2008, after Veritas and CGG merged, she became the research and processing center manager at CGG's newly opened Beijing Center. In 2015, she moved to Singapore, the APAC Hub of CGG, where she has since been in charge of multi-component and seabed imaging research. Miao has developed a variety of multi-component processing and imaging technologies and published many articles.
- North America > Canada > Manitoba (0.25)
- North America > Canada > Alberta > Census Division No. 6 > Calgary Metropolitan Region > Calgary (0.25)
- Asia > China > Beijing > Beijing (0.25)
- Information Technology > Knowledge Management (0.76)
- Information Technology > Communications > Collaboration (0.76)
Distributed acoustic sensing for seismic surface wave data acquisition in an intertidal environment
Trafford, Andrew (University College Dublin) | Ellwood, Robert (Optasense Limited, QinetiQ) | Godfrey, Alastair (Optasense Limited, Indeximate Limited) | Minto, Christopher (Optasense Limited, Indeximate Limited) | Donohue, Shane (University College Dublin)
This paper assesses the use of Distributed Acoustic Sensing (DAS) for shallow marine seismic investigations, in particular the collection of seismic surface wave data, in an intertidal setting. The paper considers appropriate selection and directional sensitivity of fiber optic cables and validates the measured data with respect to conventional seismic data acquisition approaches ,using geophones and hydrophones, along with independent borehole and Seismic Cone Penetration Test (SCPT) data. In terms of cable selection, a reduction of amplitude and frequency response of an armored cable is observed, when compared to an unarmored cable. For seismic surface wave surveys in an offshore environment where the cable would need to withstand significant stresses, the use of the armored variant with limited loss in frequency response may be acceptable, from a practical perspective. The DAS approach has also shown good consistency with conventional means of surface wave data acquisition, and the inverted Vs is also very consistent with downhole SCPT data. Observed differences in phase velocity between high tide (Scholte wave propagation) and low tide (Rayleigh wave propagation) are not thought to be related to the particular type of interface wave due to shallow water depth. These differences are more likely to be related to the development of capillary forces in the partially saturated granular medium at low tide. Overall, this study demonstrates that the proposed novel approach of DAS using seabed fiber-optic cables in the intertidal environment is capable of rapidly providing near-surface shear wave velocity data across considerable spatial scales (multi-km) at high resolution, beneficial for the design of subsea cables routes and landfall locations. The associated reduction in deployment and survey duration, when compared to conventional approaches, is particularly important when working in the marine environment due to potentially short weather windows and expensive downtime.
- Europe (1.00)
- North America > United States > Illinois > Madison County (0.24)
- Research Report > New Finding (0.68)
- Research Report > Experimental Study (0.54)
The Abu Dhabi National Oil Company (ADNOC) announced this week that its implementation of artificial intelligence (AI) technologies generated an additional half-billion dollars in value last year. The company credited more than 30 AI programs for contributing to the gains, highlighting their impact across its entire value chain. ADNOC also reported that these AI initiatives helped to prevent the emission of up to a million metric tons of CO2 from 2022 to 2023. "Artificial intelligence is one of the most important economic and social game changers of our era, and it can play a crucial role in accelerating a just, orderly, and equitable energy transition," Sultan Ahmed Al Jaber, CEO of ADNOC, stated. "At ADNOC, we have integrated artificial intelligence across our operations, from the control room to the boardroom, and it is enabling us to make smarter decisions and better protect our people and the environment."
- Government > Regional Government > Asia Government > Middle East Government > UAE Government (1.00)
- Energy > Oil & Gas (1.00)
To start the process of digital transformation in the oil production operations carried out in the Ecuadorian Oriente Basin, the methodology proposed was based on "MIT Sloan School of Management" and established for all the processes of innovation and product creation, called RWW, "Real, Win and Worth". Real case studies in Ecuador will be discussed including not only production engineering analysis but also production operations in the field with a major focus on asset surveillance. Both activities require time-consuming tasks such as field trips and well-by-well analysis, showing the transformation in the way we operate leveraging the use of data, promoting remote operations, and automating the workflows used within the production engineering department. The starting point of this implementation was the well surveillance workflow, carried out at the field level because there was no mature SCADA system. Thus, the Edge was implemented with capabilities based on Internet of Things (IoT) technology to connect the different elements of the production chain. Currently, more than 400 pieces of equipment have been connected to a unified platform, including electro-submersible pumping equipment (ESP), wells with Beam Pumping (BM), injector wells, injection pumps, high-pressure injection equipment, multiphase flow meters and others, which allowed to the mature field to integrate data, perform real-time analysis and remotely control any equipment that is connected.
- Information Technology > Sensing and Signal Processing (0.97)
- Information Technology > Communications > Networks > Sensor Networks (0.60)
- Information Technology > Architecture > Real Time Systems (0.60)
- Information Technology > Communications > Web (0.50)
The industry is now in its second year beyond the COVID-19 pandemic, and the workforce has changed in many ways, but issues facing production performance remain. Major conferences saw 116 submissions from 28 countries, with 64% regarding electrical submersible pumps (ESPs), and 18% and 8% devoted to gas lift and rod lift, respectively. Many of the papers dealt with case history successes. That is all well and good, but it doesn't necessarily drive reader interest if it can't be applied to their asset. The top three papers were selected on the guidelines of clarity of the abstract in addressing the scope, methods/procedures/process, results/observations/conclusions, and novel or additive information.
- North America > United States > Texas > Permian Basin > Yeso Formation (0.99)
- North America > United States > Texas > Permian Basin > Yates Formation (0.99)
- North America > United States > Texas > Permian Basin > Wolfcamp Formation (0.99)
- (21 more...)
ABSTRACT Inversion velocity analysis (IVA) is an image-domain method built upon the spatial scale separation of the model. Accordingly, the IVA method is performed with an iterative process composed of two minimization steps consisting of migration (inner loop) and tomography (outer loop), respectively, with each step accounting for its Hessian or not. The migration part provides the common-image gathers (CIGs) with extension in the horizontal subsurface offset. Then, the differential semblance optimization (DSO) misfit measures the focusing of the events in the CIGs, which indicates the quality of the velocity model. Commonly, the velocity updates are obtained from the DSO gradient. IVA is a modified version in which the approximate inverse replaces the adjoint of the inner loop process; in that case, the migration Hessian is approximately diagonal in the high-frequency regime. In this work, we report on implementation of the tomographic Hessian (i.e., the second derivative of the DSO misfit with respect to the background model) for the estimation of the background velocity model. We apply the second-order adjoint-state method to obtain the application of the tomographic Hessian on a vector. Then, we use the truncated-Newton (TN) method to obtain the update directions by computing approximately the application of the inverse of the tomographic Hessian on the descent direction. We also make a theoretical comparison between tomography in the IVA and full-waveform inversion contexts. Two numerical examples are used to compare, in terms of geophysical results and computational costs, the TN method with different gradient-based optimization methods applied to the IVA. A small model allows us to evaluate the eigenvalues of the tomographic Hessian, which explains the large damping needed in the TN case.
The Implementation of Big Data and Machine Learning (ML) processes as part of the Fourth Industrial Revolution (4IR) are already having a significant impact in the Oil & Gas Industry. Where direct access of streaming data to Big Data Systems is possible, the ML Algorithms are able to improve operational efficiency with a corresponding significant reduction in costs. Today the identification and reduction of both Invisible Lost Time (ILT) and Non-Productive Time (NPT) is even more critical than it has ever been in the past as many projects strive to maximise the rate of return on investments. However, many remote sites are linked to Big Data Systems through a lower bandwidth satellite system, resulting in loss of data granularity and time lags that make the response times of analytical solutions based upon ML processes too slow to be of any immediate value to the well construction process. As a consequence, it is vital to move the ML Algorithms as close to the source of the data as possible.
UK regulators met recently in Aberdeen with oil and gas producers and technology suppliers to discuss strategies to enhance the electrification of the nation's offshore platforms. Power generation accounted for almost 80% of UK offshore oil and gas emissions in 2022--or about 2 mtpa. On the whole, the upstream industry represents a 3% share of all UK greenhouse gas emissions, according to the North Sea Transition Authority (NSTA). The authority has previously voiced concerns that the domestic industry must intensify its efforts to achieve the government's goal of halving emissions from oil and gas production by 2030. "Platform electrification is a key step on the road to net zero. The North Sea has long been a testbed for pioneering technologies and right now we need innovative solutions to crack the significant challenge of electrification, cut emissions, and accelerate the transition," Bill Cattanach, the head of supply chain for NSTA, said in a statement.
- Energy > Oil & Gas > Upstream (1.00)
- Government > Regional Government > Europe Government > United Kingdom Government (0.62)
Traditionally gas hydrates avoidance has been pre-requisite in the design and operation of offshore gas production systems. Transformation towards a risk management paradigm requires collaboration between industry, government and academic research. With risk defined by the probability and consequence of each possible hydrate-based flow reduction, a combination of benchtop, pilot-scale (flowloop), and field-scale (simulation) insights presents a path to adoption of risk management in design and operational decision-making. The speaker presents the evolution of the UWA Gas-Dominant Hydrate prediction algorithms, from their birth in Perth's flowloops through recent applications in two industrially-relevant flow simulation tools. By improving the description of hydrate-based physical processes available to these tools, operators can explore whether design and operating margins (e.g.