Automatic Assessment of Distributed Temperature Data in Gas-Lift Wells

Transcription

1 Automatic Assessment of Distributed Temperature Data in Gas-Lift Wells Jeffrey Tarvin, Lalitha Venkataramanan, Tommy White Schlumberger Lee Matthews Occidental of Elk Hills, Inc.

2 Outline DTS in Gas-Lift Rapid Assessment Method Examples 2 JAT

3 Advantages of Distributed Temperature Sensing (DTS) Simultaneous acquisition of a complete temperature profile No other instrument observes the rapid fluctuations that can occur throughout a gas-lift well Real-time telemetry Temperature Other instruments Access to wells with tool size restrictions 3 JAT

4 Features of DTS Data in Gas-Lift Wells Gas (usually) cools as it expands through an orifice Temperature profiles may fluctuate wildly Temperature fluctuations can move rapidly 4 JAT Depth (m) Temperature (C) Well B gas minutes

5 Background Trends and Quantity of Data Can Hide Information Well B Temperature 5 JAT Depth (m) Time (hr)

6 Unprocessed Data May Not Show Much Depth (meters) Well B 6 JAT Temperature(C)

7 Requirements for Automated Rapid Assessment Tolerant of gas-lift characteristics Fluctuations, variable background Minimal input and intervention from operator Meaningful parameters Rapid computation Results available in real time Display that summarizes essential information 7 JAT

8 Rapid Assessment Method Operator enters valve depths Automatic processing Read DTS data Select regions for fitting for each valve depth Fit a model to data for each valve depth, in each profile Test the results of each fit for statistical significance Display results 8 JAT

9 6-Parameter Model 1. Valve depth 2. Amplitude 3 and 4. Exponential decay length (different on each side) 5 and 6. Background slope and intercept Depth (meters) z j d T j = Aexp + a + bz l i Temperature (C) j 9 JAT

10 Simple Model Fits Data Well Data Mo de l Depth (meters) JAT Te m pe rature (C)

11 Contour Plot of Model Fit Shows Valve Operating Smoothly 274 Well A Measured Depth (m) Temperature(C) 11 JAT Tim e (Ho urs ) -2

12 Temperature Amplitude Shows Open Valves During Unloading Sequence 2 12 JAT Temperature (C) m 1113 m m 1568 m m 1871 m Tim e (ho urs ) Well B

13 Well B -2 Temperature(C) 13 JAT Shallow Valve (1) Time (Ho urs ) -6 Measured Depth (m)

14 Intermediate Valve (2) 14 JAT Measured Depth (m) Time (Ho urs ) Temperature(C) Well B

15 Intermediate Valve (5) Operates Intermittently Well B Measured Depth (m) Temperature(C) JAT Tim e (Ho urs )

16 Deep Valve (6) is Open Continuously after 1 hours 178 Well B Measured Depth (m) Temperature(C) JAT Time (Ho urs )

17 DTS Shows Large Influx at Wrong Depth: Leak Causes Massive Fluctuation 194 Well C Measured Depth (m) Valve Leak Temperature(C) 17 JAT Time (Hours ) -1

18 Bottom-Hole Pressure Fluctuations Verify DTS Interpretation Time (hours) Temperature(C) Bottom Hole Pressure (psi) Measured Depth (m) JAT Well C

19 Depth Error Reveals Packer/Tubing Leak 21 Well C Depth (meters) Gas enters 25 meters below mandrel Gas at mandrel JAT Tim e (ho urs )

20 Contour Plot of Model Fit Shows Valve Chattering Well D Measured Depth (m) Temperature(C) JAT Tim e (Ho urs )

21 Conclusions DTS reveals the condition of a gas-lift well, but background trends and the large quantity of data can hide the important information Automatic processing can quickly remove trends and summarize essential information 21 JAT