Multi-Trajectory Hydraulic Model for More Accurate Geosteering Constraints

2021 ◽  
Author(s):  
Sergey Alyaev ◽  
Benoit Daireaux

Abstract At the well-planning stage target selection usually accounts for drillability. However, during geosteering operations the drilling constraints are not updated and some fixed limits in terms of maximal inclination, dogleg severity, etc., are used instead. We demonstrate a methodology that uses fast physical models of the drilling hydraulics to calculate constraints and costs for geosteering dynamically during an operation. In field development, many companies have adopted workflows that use ensemble-based methods for decision support. A real-time variation of such a decision support system (DSS) has been recently proposed for geosteering. The DSS is capable of optimization full well trajectories across all realizations of the earth model and can consider multiple objectives and constraints simultaneously. We present a method that makes steady-state hydraulic computations for all possible trajectories ahead-of-bit simultaneously at a low added cost. The output of the computation can provide more precise constraints (geo-pressure margins and cuttings transport) and cost estimates for the DSS. In this paper we focus on verification and testing of the proposed multi-trajectory hydraulic model (MTHM). Discretization of the model acts as a trade-off between the preciseness of the computation and the computational speed. On our benchmark cases, a simulation that computes the hydraulic parameters for all trajectories with acceptable errors is fast enough for real-time geo-steering applications. Furthermore, we present a case based on data from the Norwegian Continental Shelf for which we demonstrate how hydraulic computations would influence the decisions of steering and stopping. Applying the DSS with the MTHM allows to precisely update the allowed steering interval, thus achieving safe operation while maximizing the expected well profit. We emphasize that integration of the drilling processes modelling as part of the decision support for the geosteering operation enables better decisions. This is facilitated by the digitalization of the oil industry, but still requires development of new approximate models of the drilling processes. This paper demonstrates the MTHM as an initial step towards integration of drilling and geosteering modelling.

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 778-P
Author(s):  
ZIYU LIU ◽  
CHAOFAN WANG ◽  
XUEYING ZHENG ◽  
SIHUI LUO ◽  
DAIZHI YANG ◽  
...  

2018 ◽  
Author(s):  
Rivalri Kristianto Hondro ◽  
Mesran Mesran ◽  
Andysah Putera Utama Siahaan

Procurement selection process in the acceptance of prospective students is an initial step undertaken by private universities to attract superior students. However, sometimes this selection process is just a procedural process that is commonly done by universities without grouping prospective students from superior students into a class that is superior compared to other classes. To process the selection results can be done using the help of computer systems, known as decision support systems. To produce a better, accurate and objective decision result is used a method that can be applied in decision support systems. Multi-Objective Optimization Method by Ratio Analysis (MOORA) is one of the MADM methods that can perform calculations on the value of criteria of attributes (prospective students) that helps decision makers to produce the right decision in the form of students who enter into the category of prospective students superior.


Forests ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 294
Author(s):  
Nicholas F. McCarthy ◽  
Ali Tohidi ◽  
Yawar Aziz ◽  
Matt Dennie ◽  
Mario Miguel Valero ◽  
...  

Scarcity in wildland fire progression data as well as considerable uncertainties in forecasts demand improved methods to monitor fire spread in real time. However, there exists at present no scalable solution to acquire consistent information about active forest fires that is both spatially and temporally explicit. To overcome this limitation, we propose a statistical downscaling scheme based on deep learning that leverages multi-source Remote Sensing (RS) data. Our system relies on a U-Net Convolutional Neural Network (CNN) to downscale Geostationary (GEO) satellite multispectral imagery and continuously monitor active fire progression with a spatial resolution similar to Low Earth Orbit (LEO) sensors. In order to achieve this, the model trains on LEO RS products, land use information, vegetation properties, and terrain data. The practical implementation has been optimized to use cloud compute clusters, software containers and multi-step parallel pipelines in order to facilitate real time operational deployment. The performance of the model was validated in five wildfires selected from among the most destructive that occurred in California in 2017 and 2018. These results demonstrate the effectiveness of the proposed methodology in monitoring fire progression with high spatiotemporal resolution, which can be instrumental for decision support during the first hours of wildfires that may quickly become large and dangerous. Additionally, the proposed methodology can be leveraged to collect detailed quantitative data about real-scale wildfire behaviour, thus supporting the development and validation of fire spread models.


SIMULATION ◽  
2021 ◽  
pp. 003754972110216
Author(s):  
Zhang Lei ◽  
Li Jie ◽  
Wang Menglu ◽  
Liu Mengya

Simulating a physical system in real-time is widely used in equipment design, test, and validation. Though an implicit multistep numerical method excels at solving physical models that are usually composed of stiff ordinary differential equations, it is not suitable for real-time simulation because of state discontinuity and massive iterations for root finding. Thus, a method based on the backward differential formula is presented. It divides the main fixed step of real-time simulation into limited minor steps according to computing cost and accuracy demand. By analyzing and testing its capability, this method shows advantage and efficiency in real-time simulation, especially when the system contains stiff equations. A simulation application will have more flexibility while using this method.


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