REAL-TIME ENSEMBLE-BASED WELL-LOG INTERPRETATION FOR GEOSTEERING

2021 ◽  
Author(s):  
Nazanin Jahani ◽  
◽  
Joaquín Ambía ◽  
Kristian Fossum ◽  
Sergey Alyaev ◽  
...  
Keyword(s):  
Real Time ◽  
Water Saturation ◽  
Joint Estimation ◽  
Well Logs ◽  
Nuclear Density ◽  
Layer By Layer ◽  
Computationally Efficient ◽  
Parameter Uncertainties ◽  
Formation Evaluation ◽  
Look Ahead

The cost of drilling wells on the Norwegian Continen-tal Shelf are extremely high, and hydrocarbon reservoirs are often located in spatially complex rock formations. Optimized well placement with real-time geosteering is crucial to efficiently produce from such reservoirs and reduce exploration and development costs. Geosteering is commonly assisted by repeated formation evaluation based on the interpretation of well logs while drilling. Thus, reliable computationally efficient and robust work-flows that can interpret well logs and capture uncertain-ties in real time are necessary for successful well place-ment. We present a formation evaluation workflow for geosteering that implements an iterative version of an ensemble-based method, namely the approximate Leven-berg Marquardt form of the Ensemble Randomized Max-imum Likelihood (LM-EnRML). The workflow jointly estimates the petrophysical and geological model param-eters and their uncertainties. In this paper the demon-strate joint estimation of layer-by-layer water saturation, porosity, and layer-boundary locations and inference of layers’ resistivities and densities. The parameters are estimated by minimizing the statistical misfit between the simulated and the observed measurements for several logs on different scales simultaneously (i.e., shallow-sensing nuclear density and shallow to extra-deep EM logs). Numerical experiments performed on a synthetic exam-ple verified that the iterative ensemble-based method can estimate multiple petrophysical parameters and decrease their uncertainties in a fraction of time compared to clas-sical Monte Carlo methods. Extra-deep EM measure-ments are known to provide the best reliable informa-tion for geosteering, and we show that they can be in-terpreted within the proposed workflow. However, we also observe that the parameter uncertainties noticeably decrease when deep-sensing EM logs are combined with shallow sensing nuclear density logs. Importantly the es-timation quality increases not only in the proximity of the shallow tool but also extends to the look ahead of the extra-deep EM capabilities. We specifically quantify how shallow data can lead to significant uncertainty re-duction of the boundary positions ahead of bit, which is crucial for geosteering decisions and reservoir mapping.

JOINT ESTIMATION OF THE TRANSMISSIBILITY AND SEVERITY OF EBOLA VIRUS DISEASE IN REAL TIME

2017 ◽  
Vol 25 (04) ◽  
pp. 587-603 ◽  
Author(s):  
YUSUKE ASAI ◽  
HIROSHI NISHIURA
Keyword(s):  
Real Time ◽  
Ebola Virus ◽  
Virus Disease ◽  
Critical Role ◽  
Estimation Method ◽  
Case Fatality ◽  
Ascertainment Bias ◽  
Joint Estimation ◽  
Ebola Virus Disease ◽  
Strong Impact

The effective reproduction number [Formula: see text], the average number of secondary cases that are generated by a single primary case at calendar time [Formula: see text], plays a critical role in interpreting the temporal transmission dynamics of an infectious disease epidemic, while the case fatality risk (CFR) is an indispensable measure of the severity of disease. In many instances, [Formula: see text] is estimated using the reported number of cases (i.e., the incidence data), but such report often does not arrive on time, and moreover, the rate of diagnosis could change as a function of time, especially if we handle diseases that involve substantial number of asymptomatic and mild infections and large outbreaks that go beyond the local capacity of reporting. In addition, CFR is well known to be prone to ascertainment bias, often erroneously overestimated. In this paper, we propose a joint estimation method of [Formula: see text] and CFR of Ebola virus disease (EVD), analyzing the early epidemic data of EVD from March to October 2014 and addressing the ascertainment bias in real time. To assess the reliability of the proposed method, coverage probabilities were computed. When ascertainment effort plays a role in interpreting the epidemiological dynamics, it is useful to analyze not only reported (confirmed or suspected) cases, but also the temporal distribution of deceased individuals to avoid any strong impact of time dependent changes in diagnosis and reporting.

RSNet: Rail semantic segmentation network for extracting aerial railroad images

2021 ◽  
pp. 1-18
Author(s):  
R.S. Rampriya ◽  
Sabarinathan ◽  
R. Suganya
Keyword(s):  
Real Time ◽  
Visual Processing ◽  
Feature Fusion ◽  
Semantic Segmentation ◽  
Vital Role ◽  
Obstacle Detection ◽  
Aerial Images ◽  
Computationally Efficient ◽  
Fusion Algorithm ◽  
Uav Images

In the near future, combo of UAV (Unmanned Aerial Vehicle) and computer vision will play a vital role in monitoring the condition of the railroad periodically to ensure passenger safety. The most significant module involved in railroad visual processing is obstacle detection, in which caution is obstacle fallen near track gage inside or outside. This leads to the importance of detecting and segment the railroad as three key regions, such as gage inside, rails, and background. Traditional railroad segmentation methods depend on either manual feature selection or expensive dedicated devices such as Lidar, which is typically less reliable in railroad semantic segmentation. Also, cameras mounted on moving vehicles like a drone can produce high-resolution images, so segmenting precise pixel information from those aerial images has been challenging due to the railroad surroundings chaos. RSNet is a multi-level feature fusion algorithm for segmenting railroad aerial images captured by UAV and proposes an attention-based efficient convolutional encoder for feature extraction, which is robust and computationally efficient and modified residual decoder for segmentation which considers only essential features and produces less overhead with higher performance even in real-time railroad drone imagery. The network is trained and tested on a railroad scenic view segmentation dataset (RSSD), which we have built from real-time UAV images and achieves 0.973 dice coefficient and 0.94 jaccard on test data that exhibits better results compared to the existing approaches like a residual unit and residual squeeze net.

An Advanced Ultra-Deep Resistivity Mapping Sensor Reduced Reservoir Uncertainty and Eliminated the Need for a Pilot Hole for the First Time; A Case Study from Offshore Abu Dhabi

2021 ◽  
Author(s):  
Wael Fares ◽  
Islam Moustafa ◽  
Ali Al Felasi ◽  
Hocine Khemissa ◽  
Omar Al Mutwali ◽  
...  
Keyword(s):  
Real Time ◽  
Water Saturation ◽  
Firing Frequency ◽  
Target Zones ◽  
The Real ◽  
Pilot Hole ◽  
Low Resistivity ◽  
Milling Operations ◽  
The Future ◽  
Identify Formation

Abstract The high reservoir uncertainty, due to the lateral distribution of fluids, results in variable water saturation, which is very challenging in drilling horizontal wells. In order to reduce uncertainty, the plan was to drill a pilot hole to evaluate the target zones and plan horizontal sections based on the information gained. To investigate the possibility of avoiding pilot holes in the future, an advanced ultra-deep resistivity mapping sensor was deployed to map the mature reservoirs, to identify formation and fluid boundaries early before penetrating them, avoiding the need for pilot holes. Prewell inversion modeling was conducted to optimize the spacing and firing frequency selection and to facilitate an early real-time geostopping decision. The plan was to run the ultra-deep resistivity mapping sensor in conjunction with shallow propagation resistivity, density, and neutron porosity tools while drilling the 8 ½-in. landing section. The real-time ultra-deep resistivity mapping inversion was run using a depth of inversion up to 120 ft., to be able to detect the reservoir early and evaluate the predicted reservoir resistivity. This would allow optimization of any geostopping decision. The ultra-deep resistivity mapping sensor delivered accurate mapping of low resistivity zones up to 85 ft. TVD away from the wellbore in a challenging low resistivity environment. The real-time ultra-deep resistivity mapping inversion enabled the prediction of resistivity values in target zones prior to entering the reservoir; values which were later crosschecked against open-hole logs for validation. The results enabled identification of the optimal geostopping point in the 8 ½-in. section, enabling up to seven rig days to be saved in the future by eliminating a pilot hole. In addition this would eliminate the risk of setting a whipstock at high inclination with the subsequent impact on milling operations. In specific cases, this minimizes drilling risks in unknown/high reservoir pressure zones by improving early detection of formation tops. Plans were modified for a nearby future well and the pilot-hole phase was eliminated because of the confidence provided by these results. Deployment of the ultra-deep resistivity mapping sensor in these mature carbonate reservoirs may reduce the uncertainty associated with fluid migration. In addition, use of the tool can facilitate precise geosteering to maintain distance from fluid boundaries in thick reservoirs. Furthermore, due to the depths of investigation possible with these tools, it will help enable the mapping of nearby reservoirs for future development. Further multi-disciplinary studies remain desirable using existing standard log data to validate the effectiveness of this concept for different fields and reservoirs.

Real-Time EM Look-Ahead Resistivity for Mapping of Oil/Water Contact While Drilling at Low Deviated Well, New Perspective for Mature Oil Fields Development: A Case Study Of Umm Gudair Field, Kuwait

2021 ◽  
Author(s):  
Nasser Faisal Al-Khalifa ◽  
Mohammed Farouk Hassan ◽  
Deepak Joshi ◽  
Asheshwar Tiwary ◽  
Ihsan Taufik Pasaribu ◽  
...  
Keyword(s):  
Water Saturation ◽  
High Water ◽  
Carbonate Reservoir ◽  
Water Contact ◽  
Saturation Zone ◽  
Look Ahead ◽  
Production History ◽  
Oil Water ◽  
High Water Cut ◽  
Water Cut

Abstract The Umm Gudair (UG) Field is a carbonate reservoir of West Kuwait with more than 57 years of production history. The average water cut of the field reached closed to 60 percent due to a long history of production and regulating drawdown in a different part of the field, consequentially undulating the current oil/water contact (COWC). As a result, there is high uncertainty of the current oil/water contact (COWC) that impacts the drilling strategy in the field. The typical approach used to develop the field in the lower part of carbonate is to drill deviated wells to original oil/water contact (OOWC) to know the saturation profile and later cement back up to above the high-water saturation zone and then perforate with standoff. This method has not shown encouraging results, and a high water cut presence remains. An innovative solution is required with a technology that can give a proactive approach while drilling to indicate approaching current oil/water contact and geo-stop drilling to give optimal standoff between the bit and the detected water contact (COWC). Recent development of electromagnetic (EM) look-ahead resistivity technology was considered and first implemented in the Umm Gudair (UG) Field. It is an electromagnetic-based signal that can detect the resistivity features ahead of the bit while drilling and enables proactive decisions to reduce drilling and geological or reservoir risks related to the well placement challenges.

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