REAL-TIME 3D IMAGING OF COMPLEX TURBIDITIC RESERVOIR ARCHITECTURE

2021 ◽  
Author(s):  
Supriya Sinha ◽  
◽  
Karol Riofrio ◽  
Arthur Walmsley ◽  
Nigel Clegg ◽  
...  

Siliciclastic turbidite lobes and channels are known to exhibit varying degrees of architectural complexity. Understanding the elements that contribute to this complexity is the key to optimizing drilling targets, completions designs and long-term production. Several methods for 3D reservoir modelling based on seismic and electromagnetic (EM) data are available that are often complemented with outcrop, core and well log data studies. This paper explores an ultra-deep 3D EM inversion process during real-time drilling and how it can enhance the reservoir understanding beyond the existing approaches. The new generation of ultra-deep triaxial EM logging tools provide full-tensor, multi-component data with large depths of detection, allowing a range of geophysical inversion processing techniques to be implemented. A Gauss-Newton-based 3D inversion using semi-structured meshing was adapted to support real-time inversion of ultra-deep EM data while drilling. This 3D processing methodology provides more accurate imaging of the 3D architectural elements of the reservoir compared to earlier independent up-down, right-left imaging using 1D and 2D processing methods. This technology was trialed in multiple wells in the Heimdal Formation, a siliciclastic Palaeocene reservoir in the North Sea. The Heimdal Fm. sandstones are generally considered to be of excellent reservoir quality, deposited through many turbiditic pulses of variable energy. The presence of thin intra-reservoir shales, fine-grained sands, heterolithic zones and calcite-cemented intervals add architectural complexity to the reservoir and subsequently impacts the fluid flow within the sands. These features are responsible for heterogeneities that create tortuosity in the reservoir. When combined with more than a decade of production, they have caused significant localized movement of oil-water and gas-oil contacts. Ultra-deep 3D EM measurements have sensitivity to both rock and fluid properties within the EM field volume. They can, therefore, be applied to mapping both the internal reservoir structure and the oil-water contacts in the field. The enhanced imaging provided by the 3D inversion technology has allowed the interpretation of what appears to be laterally stacked turbidite channel fill deposits within a cross-axial amalgamated reservoir section. Accurate imaging of these elements has provided strong evidence of this depositional mechanism for the first time and added structural control in an area with little or no seismic signal.

2021 ◽  
Vol 2 (1) ◽  
pp. 336-344
Author(s):  
Anna S. Astrakova ◽  
Elena V. Konobriy ◽  
Dmitry Yu. Kushnir ◽  
Nikolay N. Velker ◽  
Gleb V. Dyatlov

Non-structural traps and reservoir flanks are characterized by angular unconformities. Angular unconformity between dipping formation and sub-horizontal oil-water contact is common in the North Sea fields. This paper presents an approach to real-time inversion of LWD resistivity data for the scenario with angular unconformity. The approach utilizes artificial neural networks (ANNs) for calculating the tool responses in parametric surface-based 2D resistivity models. We propose a parametric model with two non-parallel boundaries suitable for scenarios with angular unconformity and pinch-out. Training of ANNs for this parametric model is performed using a database containing samples with the model parameters and corresponding tool responses. ANNs are the kernel of 2D inversion based on the Levenberg-Marquardt optimization method. To demonstrate applicability of our approach and compare with the results of 1D inversion, we analyze Extra Deep Azimuthal Resistivity tool responses in a 2D synthetic model. It is shown that 1D inversion determines either the position of the oil-water contact or dipping layers structure. At the same time, 2D inversion makes it possible to correctly reconstruct the positions of non-parallel boundaries. Performance of 2D inversion based on ANNs is suitable for real-time applications.


2021 ◽  
Author(s):  
Alexander Valentin Goertz ◽  
Tatiana Thiem ◽  
Endre Vange Bergfjord ◽  
Audun Libak ◽  
Brian Atkinson ◽  
...  

Abstract We monitor the seismic signal emitted from a rotating drill bit in real time with an array of seismic sensors at the seafloor. Drill-bit seismic signals provide information to locate the drill-bit position itself and to image geological objects ahead and around the drill bit for geosteering purposes during drilling operations. The data can be obtained in real time without the need to stop drilling for logging and without any additional downhole instrumentation in the bottom hole assembly. Drill-bit positioning accuracy is independent of measured depth and with meter level lateral precision. This is significantly better than conventional downhole gyro-based methods, especially for long horizontal wells. With sources along the drilled well path approaching a target reservoir we obtain a 3D reverse VSP (RVSP) image around the well for prediction ahead of the drill bit. This paper presents a case study from the Grane reservoir in the North Sea, where we utilize a permanent reservoir monitoring (PRM) array for listening to signals emitted from drilling with a PDC bit. We present imaging results from a highly deviated well and compare them to 3D seismic. The field example shows the ability to look ahead several hundreds of meters below the drilled well trajectory.


1981 ◽  
Vol 71 (4) ◽  
pp. 1351-1360
Author(s):  
Tom Goforth ◽  
Eugene Herrin

abstract An automatic seismic signal detection algorithm based on the Walsh transform has been developed for short-period data sampled at 20 samples/sec. Since the amplitude of Walsh function is either +1 or −1, the Walsh transform can be accomplished in a computer with a series of shifts and fixed-point additions. The savings in computation time makes it possible to compute the Walsh transform and to perform prewhitening and band-pass filtering in the Walsh domain with a microcomputer for use in real-time signal detection. The algorithm was initially programmed in FORTRAN on a Raytheon Data Systems 500 minicomputer. Tests utilizing seismic data recorded in Dallas, Albuquerque, and Norway indicate that the algorithm has a detection capability comparable to a human analyst. Programming of the detection algorithm in machine language on a Z80 microprocessor-based computer has been accomplished; run time on the microcomputer is approximately 110 real time. The detection capability of the Z80 version of the algorithm is not degraded relative to the FORTRAN version.


2020 ◽  
Author(s):  
Rosario Megna

Abstract Background : The first outbreaks of COVID-19 in Italy occurred during the second half of February 2020 in some areas in the North of the country. Due to the high contagiousness of the infection, further spread by asymptomatic people, Italy has become in a few weeks the country with the greatest number of infected people in the World. The large number of severe cases among infected people in Italy led to the hospitalization of thousands of patients, with a heavy burden on the National Health Service. Methods: We analyzed data provided daily by Italian Authorities for the period from 24 February 2020 to 30 March 2020. Considering such information, we developed a forecast model in real-time, based on the cumulative logistic distribution. We then produced an estimate of the overall number of potentially infected individuals and epidemic duration at a national and Regional level, for the most affected Regions. Results: A total of 101,739 infected individuals was confirmed until 30 March 2020, of which 75,528 active cases, 14,620 recovered or discharged, and 11,591 deaths. Until the same date patients quarantined at home were 43,752, whereas hospitalized patients were 31,776, of which 3,981 in intensive care. The forecast model estimated a number of infected persons for Italy of 130,000 about, and a duration of the epidemic greater than 2 months. Conclusions : Once month after the first outbreaks there seems to be the first signs of a decrease in the number of infections, showing that we could be now facing the descending phase of the epidemic. The forecast obtained thanks to our model could be used by decision-makers to implement coordinative and collaborative efforts in order to control the epidemic. The pandemic due to novel Coronavirus must be a warning for all countries worldwide, regarding a rapid and complete dissemination of information, surveillance, health organization, and cooperation among the states.


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