Real Time Well Testing: A Game Changer – Experience and Lessons Learned Over 100 Well Tests Performed In the North Sea

Hollaender, Florian; Shumakov, Yakov; Sarac, Sukru; Theuveny, Bertrand

doi:10.2118/176808-ms

Cited by 15 publications

(1 citation statement)

References 11 publications

(9 reference statements)

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“…In fact, a lot of the fields across the globe have undergone digital transformation which have not only enabled them to make more effective decision making but have also reduced cost and efforts significantly. Real time well testing has become more realistic in the last 5 years from the analysis perspective, thanks to the everevolving technology (14)(15)(16). The machine learning model developed in this work can very well be updated on real time basis if the model has access to the real-time data.…”

Section: Ecs Transactions 107 (1) 6587-6598 (2022)mentioning

confidence: 99%

A Machine Learning Approach Towards Predicting Formation Permeability Using Real-Time Data

Nande¹,

Patwardhan²

2022

ECS Trans.

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Reservoirs are hydrocarbons (oil and gas) bearing subsurface structures (formation) in which wells are drilled to produce the fluids to the surface. Well testing is a method of studying pressures and their corresponding rates from an individual well, to analyze the various characteristics of a reservoir, which helps in optimum management of the production operations. In this paper, we have applied one of the most popular supervised learning algorithms known as Artificial Neural Networks (ANN) for predicting the permeability (conductivity) of the formation. The well testing data consisting of well head pressure, down hole gauge pressure, flow rates for oil and water, P*, P-1hour, etc. were used as input features for training the model. A 4-layer dense ANN architecture consisting of one input and output layer each and two hidden layers was built for training and testing the model.

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Section: Ecs Transactions 107 (1) 6587-6598 (2022)mentioning

confidence: 99%

A Machine Learning Approach Towards Predicting Formation Permeability Using Real-Time Data

Nande¹,

Patwardhan²

2022

ECS Trans.

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Temperature Transients Affect Reservoir Pressure Estimation During Well Tests - Case Study and Model

Maizeret

Sidorova

Lipovetsky

2016

Day 3 Wed, September 28, 2016

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This paper discusses how the pressure measured by the downhole gauges during a drillstem test (DST) is affected not only by hydrostatic, friction or kinetic energy pressure losses, but also by changes in temperature. In oil wells, the bottomhole temperature is decreasing when the pressure is building up after a flow period. The change in temperature is inducing a change in the wellbore fluid density, itself affecting the bottomhole pressure. Most numerical simulators modeling flow in pipes do not take into account this effect, as it is not significant for low to moderate productivity wells. In high productivity wells however, the temperature change can affect drastically the bottomhole pressure measured above the reservoir and the pressure can even decrease at the end of a static period. A mathematical model is presented to compute the pressure drop caused by the nonisothermal effects and can be used to correct the measurements. Field examples are discussed to illustrate this effect and recommendations are made on how new technology can improve pressure measurements during well tests. In order to measure the effects on the pressure measurements due to the position of the gauges in the DST string, two gauge carriers were run in some well tests in deepwater wells offshore Brazil, one at the level of the tester valve, and one below packer, as deep as possible in the test string. The gauges could transmit data with a wireless telemetry system that allowed the operator to monitor in real-time the pressure changes and the difference between the two sets of gauges. In high permeability wells, using the uncorrected pressure measured with a gauge 50 to 100 m above the reservoir as is typically the case, can result in a wrong productivity index and at times, in uninterpretable results. Locating pressure gauges as close to the reservoir as possible is crucial in highpermeability reservoirs, to minimize the measurement errors, but also to avoid misinterpretations when an obstruction forms below the tester valve. With the new wireless telemetry systems, it is possible to monitor in realtime gauges mounted below the packer, much closer to the perforations than the standard gauges placed at the level of the tester valve, thus avoiding taking wrong decisions during the test. A long pressure transient test could be uninterpretable because the gauges are located at the wrong depth. Ideally, the pressure gauges should be placed in front of the perforations but it is not always possible. Therefore, non-isothermal effects on pressure measurements during a well test needs to be clearly understood and taken into account when designing and interpreting well tests, particularly in high productivity reservoirs.

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Temperature Transients Affect Reservoir–Pressure Estimation During Well Tests: Case Study and Model

Maizeret

Sidorova

Lipovetsky

2017

SPE Reservoir Evaluation &Amp; Engineering

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Summary This paper discusses how the pressure measured by the downhole gauges during a drillstem test (DST) is affected not only by hydrostatic, friction, or kinetic-energy pressure losses, but also by changes in temperature. In oil wells, the bottomhole temperature is decreasing when the pressure is building up after a flow period. The change in temperature is inducing a change in the wellbore-fluid density, itself affecting the bottomhole pressure. Most numerical-simulators modeling flow in pipes do not take into account this effect, because it is not significant for low- to moderate-productivity wells. In high-productivity wells, however, the temperature change can affect dramatically the bottomhole pressure measured above the reservoir, and the pressure can even decrease at the end of a static period. A mathematical model is presented to compute the pressure drop caused by the nonisothermal effects, and it can be used to correct the measurements. Field examples are discussed to illustrate this effect, and recommendations are made on how new technology can improve pressure measurements during well tests. To measure the effects on the pressure measurements caused by the position of the gauges in the DST string, two gauge carriers were run in some well tests in deepwater wells offshore Brazil, one at the level of the tester valve and one below the packer, as deep as possible in the test string. The gauges could transmit data with a wireless telemetry system that allowed the operator to monitor in real time the pressure changes and the difference between the two sets of gauges. In high-permeability wells, using the uncorrected pressure measured with a gauge 50 to 100 m above the reservoir, as is typically the case, can result in a wrong productivity index (PI) and, at times, in uninterpretable results. Locating pressure gauges as close to the reservoir as possible is crucial in high-permeability reservoirs, to minimize the measurement errors, but also to avoid misinterpretations when an obstruction forms below the tester valve. With the new wireless telemetry systems, it is possible to monitor in real time gauges mounted below the packer, much closer to the perforations than the standard gauges placed at the level of the tester valve, thus avoiding making wrong decisions during the test. A long pressure-transient test could be uninterpretable because the gauges are at the wrong depth. Ideally, the pressure gauges should be placed in front of the perforations, but it is not always possible. Therefore, nonisothermal effects on pressure measurements during a well test need to be clearly understood and taken into account when designing and interpreting well tests, particularly in high-productivity reservoirs.

show abstract

Real Time Well Testing: A Game Changer – Experience and Lessons Learned Over 100 Well Tests Performed In the North Sea

Cited by 15 publications

References 11 publications

A Machine Learning Approach Towards Predicting Formation Permeability Using Real-Time Data

A Machine Learning Approach Towards Predicting Formation Permeability Using Real-Time Data

Temperature Transients Affect Reservoir Pressure Estimation During Well Tests - Case Study and Model

Temperature Transients Affect Reservoir–Pressure Estimation During Well Tests: Case Study and Model

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