Rate Allocation: Combining Transient Well Flow Modeling and Data Assimilation

Lorentzen, Rolf Johan; Sævareid, Ove; Nævdal, Gunnar

doi:10.2118/135073-ms

Cited by 12 publications

(8 citation statements)

References 18 publications

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“…It has been demonstrated that in contrast with optimization algorithms, the Kalman filtering is more capable of dealing with sufficiently noisy data. Lorentzen et al [10] combined transient downhole pressure and temperature data in a soft-sensor based on the ensemble Kalman filter and a transient wellbore model. They have indicated that changing influx conditions can be detected from downhole temperature observations located close to influx zones.…”

Section: Introductionmentioning

confidence: 99%

Soft-sensors: Model-based estimation of inflow in horizontal wells using the extended Kalman filter

Gryzlov

Schiferli

Mudde

2013

Flow Measurement and Instrumentation

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Section: Introductionmentioning

confidence: 99%

Soft-sensors: Model-based estimation of inflow in horizontal wells using the extended Kalman filter

Gryzlov

Schiferli

Mudde

2013

Flow Measurement and Instrumentation

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“…With the need of monitoring of down-hole multi-phase flow and the increase in number of wells equipped with PDHGs, DTS and DPS to acquire real-time down-hole pressure and temperature measurements, interest are growing to use these measurements to estimate the down-hole multi-phase flow by means of soft sensing (Naevdal et al 2001;Leskens et al 2008;de Kruif et al 2008;Lorentzen et al 2010). Naevdal et al (2001) has performed uncertainty analysis to search for the optimal location and type of sensors to be used for estimating the inflow profile of the well having three producing zones using steady-state three-phase flow simulator.…”

Section: Introductionmentioning

confidence: 99%

Optimal Sensor Placement for Multi-Phase Flow Rate Estimation Using Pressure and Temperature Measurements

et al. 2013

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This paper discusses on uncertainty and optimal sensor placement for estimating multi-phase flow rates from the producing well by inversely solving the well flow model, given the down-hole temperature and pressure measurements. Uncertainty of the estimated multi-phase flow rates are evaluated by solving the error propagation from the given uncertainties of pressure and temperature measurements used to make the estimation. The uncertainties of the multi-phase flow rate estimations are studied for different sensor combination and location to search for the optimal sensor placement which gives the lowest uncertainty for multi-phase flow rate estimations. From the simulation study, preferred sensor combination and location of pressure and temperature measurements are found for typical oil gas 2-phase vertical well case. The methodology can be applied for reliable down-hole multi-phase flow monitoring and optimal sensor placement design for monitoring the multiphase flow rate using temperature and pressure measurements within an allowable estimation uncertainty and sensor cost.

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“…These rates are then allocated to individual wells based on either well rate test and downtime or well rate models supported by real time data. The relative discrepancy between the measured fieldwide rate and the summation of all calculated rates from the individual wells is called allocation factor or field factors (Lorentzen et al, 2010) for oil, water, and gas, respectively. The allocated well rates are obtained by adjusting the calculated rates by the allocation factors.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Production Allocation in Intelligent Wells via Application of Models and Real-Time Surveillance Data

Wu¹,

Humphrey²,

Liao³

2012

All Days

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The accuracy and precision of well rates are paramount in reservoir management, well performance surveillance, flow assurance, and any third party processing arrangements. Rate allocation is traditionally based on well rate tests and downtime. This method is usually time-consuming and thus performed relatively infrequently. This could be inadequate for proactive asset management especially with wells that may produce in transient state.This paper discusses methodology to improve well rate allocation quality and save engineering time. In practice, many fields face some or all of the following challenges that are related to well rate allocation: 1) reservoir communication, 2) well interference, 3) changing skin factors or other near wellbore boundaries, 4) uncertainties around reservoir fluid properties, and 5) difficulty in obtaining well rate tests for a variety of reasons. For many intelligent wells, it is a common practice to install permanent downhole gauges which are playing critical roles in field management. This paper describes a framework on how to capitalize on the real time data from well pressure and temperature sensors and use the data in Integrated Asset Modeling (IAM) to allocate the well rate with enhanced accuracy, increased frequency, and reduced processing time.The paper uses Atlantis in Gulf of Mexico as an example to demonstrate this process. The real-time data supported model based allocation process becomes virtual flow meters for the intelligent wells. For Atlantis, field-wide allocation accuracy has been improved from previously +/-10% error using the traditional allocation method based on well rate test and downtime method to current +/-3% error using the new method. This paper also shows model maintenance is a journey that needs strenuous attention especially after water injections commences and more production wells come online.

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Rate Allocation: Combining Transient Well Flow Modeling and Data Assimilation

Cited by 12 publications

References 18 publications

Soft-sensors: Model-based estimation of inflow in horizontal wells using the extended Kalman filter

Soft-sensors: Model-based estimation of inflow in horizontal wells using the extended Kalman filter

Optimal Sensor Placement for Multi-Phase Flow Rate Estimation Using Pressure and Temperature Measurements

Enhancing Production Allocation in Intelligent Wells via Application of Models and Real-Time Surveillance Data

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