Simultaneous Interpretation of Pressure, Temperature, and Flow-Rate Data Using Bayesian Inversion Methods

2010 ◽  
Vol 14 (02) ◽  
pp. 225-238 ◽  
Author(s):  
Obinna O. Duru ◽  
Roland N. Horne
Keyword(s):  
Flow Rate ◽  
Bayesian Methods ◽  
Temperature Measurements ◽  
Bayesian Inversion ◽  
Rate Data ◽  
Simultaneous Interpretation ◽  
Data Limitations ◽  
Regularization Parameters ◽  
Deconvolution Problem ◽  
Downhole Measurements

Summary Current downhole measuring technologies have provided a means of acquiring downhole measurements of pressure, temperature, and sometimes flow-rate data. Jointly interpreting all three measurements provides a way to overcome data limitations that are associated with interpreting only two measurements—pressure and flow-rate data—as is currently done in pressure-transient analysis. This work shows how temperature measurements can be used to improve estimations in situations where lack of sufficient pressure or flow-rate data makes parameter estimation difficult or impossible. The model that describes the temperature distribution in the reservoir lends itself to quasilinear approximations. This makes the model a candidate for Bayesian inversion. The model that describes the pressure distribution for a multirate flow system is also linear and a candidate for Bayesian inversion. These two conditions were exploited in this work to present a way to cointerpret pressure and temperature signals from a reservoir. Specifically, the Bayesian methods were applied to the deconvolution of both pressure and temperature measurements. The deconvolution of the temperature measurements yielded a vector that is linearly related to the average flow-rate from the reservoir and, hence, could be used for flow-rate estimation, especially in situations in which flow-rate measurements are unavailable or unreliable. This flow rate was shown to be sufficient for a first estimation of the pressure kernel in the pressure-deconvolution problem. When the appropriate regularization parameters are chosen, the Bayesian methods can be used to suppress fluctuations and noise in measurements while maintaining sufficient resolution of the estimates. This is the point of the application of the method to data denoising. In addition, because Bayesian statistics represent a state of knowledge, it is easier to incorporate certain information, such as breakpoints, that may help improve the structure of the estimates. The methods also lend themselves to formulations that make possible the estimation of initial properties, such as initial pressures.

scholarly journals Artificial Neural Network for Daily Low Stream Flow Rate Prediction of Perigiali Stream, Kavala City, NE Greece

Proceedings ◽  
2018 ◽  
Vol 2 (11) ◽  
pp. 578
Author(s):  
Thomas Papalaskaris ◽  
Theologos Panagiotidis
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Flow Rate ◽  
Stream Flow ◽  
Low Flow ◽  
Urban Stream ◽  
Neural Network Modeling ◽  
Rate Data ◽  
Artificial Neural

Only a few scientific research studies with reference to extremely low stream flow conditions, have been conducted in Greece, so far. Forecasting future low stream flow rate values is a crucial and desicive task when conducting drought and watershed management plans, designing water reservoirs and general hydraulic works capacity, calculating hydrological and drought low flow indices, separating groundwater base flow and storm flow of storm hydrographs etc. Artificial Neural Network modeling simulation method generates artificial time series of simulated values of a random (hydrological in this specific case) variable. The present study produces artificial low stream flow time series of both a part of the past year (2016) as well as the present year (2017) considering the stream flow data observed during two different respecting interval period of the years 2016 and 2017. We compiled an Artificial Neural Network to simulate low stream flow rate data, acquired at a certain location of the partly regulated semi-urban stream which runs through the eastern exit of Kavala city, NE Greece, using a 3-inches U.S.G.S. modified portable Parshall flume, a 3-inches conventional portable Parshall flume, a 3-inches portable Montana (short Parshall) flume and a 90° V-notched triangular shaped sharp crested portable weir plate. The observed data were plotted against the predicted one and the results were demonstrated through interactive tables providing us the ability to effectively evaluate the ANN model simulation procedure performance. Finally, we plot the recorded against the simulated low stream flow rate data, compiling a log-log scale chart which provides a better visualization of the discrepancy ratio statistical performance metrics and calculate the derived model statistics featuring the comparison between the recorded and the forecasted low stream flow rate data.

scholarly journals Forecasting Low Stream Flow Rate Using Monte—Carlo Simulation of Perigiali Stream, Kavala City, NE Greece

Proceedings ◽  
2018 ◽  
Vol 2 (11) ◽  
pp. 580 ◽  
Author(s):  
Thomas Papalaskaris ◽  
Theologos Panagiotidis
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Flow Rate ◽  
Stream Flow ◽  
Simulation Method ◽  
Base Flow ◽  
Low Flow ◽  
Rate Data ◽  
Storm Flow ◽  
Management Plans

A small number of scientific research studies with reference to extremely low flow conditions, have been conducted in Greece, so far. Predicting future low stream flow rate values is an essential and of paramount importance task when compiling watershed and drought management plans, designing water reservoirs and general hydraulic works capacity, calculating hydrological and drought low flow values, separating groundwater base flow and storm flow of storm hydrographs etc. The Monte-Carlo simulation method generates multiple attempts to define the anticipated value of a random (hydrological in this specific case) variable. The present study compiles, correspondingly, artificial low stream flow time series of both the same part of the year (2016) as well as a part of the calendar year (2017), based on the stream flow data observed during the same two different interval periods of the years 2016 and 2017, using a 3-inches U.S.G.S. modified portable Parshall flume, a 3-inches conventional portable Parshall flume, a 3-inches portable Montana (short Parshall) flume and a 90° V-notched triangular shaped sharp crested portable weir plate. The recorded data were plotted against the fitted one and the results were demonstrated through interactive tables providing us the ability to effectively evaluate the simulation procedure performance. Finally, we plot the observed against the calculated low stream flow rate data, compiling a log-log scale chart which provides a better visualization of the discrepancy ratio statistical performance metric and calculate statistics featuring the comparison between the recorded and the forecasted low stream flow rate data.

Model-Based Evaluation of Eustachian Tube Mechanical Properties Using Continuous Pressure–Flow Rate Data

2002 ◽  
Vol 30 (8) ◽  
pp. 1064-1076 ◽  
Author(s):  
Samir N. Ghadiali ◽  
J. Douglas Swarts ◽  
William J. Federspiel
Keyword(s):  
Mechanical Properties ◽  
Eustachian Tube ◽  
Flow Rate ◽  
Rate Data ◽  
Pressure Flow ◽  
Model Based

scholarly journals A Distributed Flow Rate Control Algorithm for Networked Agent System with Multiple Coding Rates to Optimize Multimedia Data Transmission

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Shuai Zeng ◽  
Lemin Li ◽  
Dan Liao
Keyword(s):  
Flow Rate ◽  
Rate Control ◽  
Data Transmission ◽  
Control Algorithm ◽  
Multimedia Data ◽  
Rate Data ◽  
Agent System ◽  
Rate Control Algorithm ◽  
Multimedia Data Transmission ◽  
Flow Rate Control

With the development of wireless technologies, mobile communication applies more and more extensively in the various walks of life. The social network of both fixed and mobile users can be seen as networked agent system. At present, kinds of devices and access network technology are widely used. Different users in this networked agent system may need different coding rates multimedia data due to their heterogeneous demand. This paper proposes a distributed flow rate control algorithm to optimize multimedia data transmission of the networked agent system with the coexisting various coding rates. In this proposed algorithm, transmission path and upload bandwidth of different coding rate data between source node, fixed and mobile nodes are appropriately arranged and controlled. On the one hand, this algorithm can provide user nodes with differentiated coding rate data and corresponding flow rate. On the other hand, it makes the different coding rate data and user nodes networked, which realizes the sharing of upload bandwidth of user nodes which require different coding rate data. The study conducts mathematical modeling on the proposed algorithm and compares the system that adopts the proposed algorithm with the existing system based on the simulation experiment and mathematical analysis. The results show that the system that adopts the proposed algorithm achieves higher upload bandwidth utilization of user nodes and lower upload bandwidth consumption of source node.

Modeling Reservoir Temperature Transients and Reservoir-Parameter Estimation Constrained to the Model

2010 ◽  
Vol 13 (06) ◽  
pp. 873-883 ◽  
Author(s):  
Obinna O. Duru ◽  
Roland N. Horne
Keyword(s):  
Flow Rate ◽  
Oil And Gas ◽  
Mechanistic Model ◽  
Operator Splitting ◽  
Temperature Data ◽  
Temperature Measurements ◽  
Solution Technique ◽  
Measured Temperature ◽  
Reservoir Temperature ◽  
Temperature Transient

Summary Permanent downhole gauges (PDGs) provide a continuous source of downhole pressure, temperature, and sometimes flow-rate data. Until recently, the measured temperature data have been largely ignored, although a close observation of the temperature measurements reveals a response to changes in flow rate and pressure. This suggests that the temperature measurements may be a useful source of reservoir information. In this study, reservoir temperature-transient models were developed for single- and multiphase-fluid flows, as functions of formation parameters, fluid properties, and changes in flow rate and pressure. The pressure fields in oil- and gas-bearing formations are usually transient, and this gives rise to pressure/temperature effects appearing as temperature change. The magnitudes of these effects depend on the properties of the formation, flow geometry, time, and other factors and result in a reservoir temperature distribution that is changing in both space and time. In this study, these thermometric effects were modeled as convective, conductive, and transient phenomena with consideration for time and space dependencies. This mechanistic model included the Joule-Thomson effects resulting from fluid compressibility and viscous dissipation in the reservoir during fluid flow. Because of the nature of the models, the semianalytical solution technique known as operator splitting was used to solve them, and the solutions were compared to synthetic and real temperature data. In addition, by matching the models to different temperature-transient histories obtained from PDGs, reservoir parameters such as average porosity, near-well permeabilities, saturation, and some thermal properties of the fluid and formation could be estimated. A key target of this work was to show that temperature measurements, often ignored, can be used to estimate reservoir parameters, as a complement to other more-conventional techniques.

Sign in / Sign up

Export Citation Format

Share Document