Processing and interpretation of real-time data from downhole and surface sensors
In accordance with an embodiment of the present invention, a method of processing large volumes of data to allow for real-time reservoir management is disclosed, comprising: a) acquiring a first data series from a first reservoir sensor; b) establishing a set of criteria based on reservoir management objectives, sensor characteristics, sensor location, nature of the reservoir, and data storage optimization, etc.; c) identifying one or more subsets of the first data series meeting at least one of the criteria; and optionally d) generating one or more second data series based on at least one of the subsets. This methodology may be repeated for numerous reservoir sensors. This methodology allows for intelligent evaluation of sensor data by using carefully established criteria to intelligently select one or more subsets of data. In an alternative embodiment, sensor data from one or more sensors may be evaluated while processing data from a different sensor.
1. A computer-implemented method for processing raw reservoir data to reduce data size, the method comprising:
i. receiving a first series of raw data as a function of time from a first reservoir sensor;
ii. receiving a second series of raw data as a function of time from a second reservoir sensor;
iii. using a predetermined criteria to identify a plurality of subsets-of-interest within the first series of raw data;
iv. using time intervals associated with the plurality of subsets-of-interest within the first series of raw data to identify corresponding subsets-of-interest within the second series of raw data; and
v. generating a third series of data as a function of time using the second series of raw data comprising the corresponding subsets-of-interest, wherein the third series of data comprises a first data resolution for the corresponding subsets-of-interest and a second data resolution that is different from the first data resolution for data outside the corresponding subsets-of-interest.
2. The computer-implemented method of claim 1 , wherein the first data resolution is greater than the second data resolution.
3. The computer-implemented method of claim 2 , wherein the third series of data omits data outside the corresponding subsets-of-interest.
4. The computer-implemented method of claim 2 , wherein the second series of raw data includes an original data resolution and the first data resolution is the same as the original data resolution.
5. The computer-implemented method of claim 1 , further comprising:
repeating processes (ii), (iv), and (v) for at least one other reservoir sensor.
6. The computer-implemented method of claim 1 , wherein the first reservoir sensor is a valve sensor.
7. The computer-implemented method of claim 1 , wherein the second reservoir sensor is a pressure sensor.
8. The computer-implemented method of claim 1 , wherein the third series of data comprises averaged data outside the corresponding subsets-of-interest.
9. The computer-implemented method of claim 1 , further comprising:
compressing the first series of raw data and the second series of raw data.
10. The computer-implemented method of claim 1 , further comprising:
acquiring the second series of raw data from the second reservoir sensor at an acquisition rate.
11. The computer-implemented method of claim 10 , further comprising:
temporarily increasing the acquisition rate when the first series of raw data meets the predetermined criteria.
12. The computer-implemented method of claim 1 , wherein the predetermined criteria includes a plurality of criteria.
13. The computer-implemented method of claim 1 , wherein the predetermined criteria is selected from the group consisting of
a threshold temperature,
a threshold pressure,
a threshold pressure gradient,
threshold sensor noise,
an opened valve,
a closed valve, and
some combination thereof.
14. The computer-implemented method of claim 1 , further comprising:
adjusting the predetermined criteria.
15. The computer-implemented method of claim 1 , further comprising:
time stamping the first series of raw data and the second series of raw data.
16. The computer-implemented method of claim 1 , further comprising:
displaying at least a portion of the third series of data using a computer or portable device.
17. The computer-implemented method of claim 1 , further comprising:
displaying at least a portion of the third series of data as a plot.
18. The computer-implemented method of claim 1 , further comprising:
interpreting at least a portion of the third series of data to derive a reservoir parameter.
19. The computer-implemented method of claim 18 , further comprising:
interpreting at least a portion of the third series of data to determine a change in the derived reservoir parameter.
20. The computer-implemented method of claim 18 , further comprising:
tracking the derived reservoir parameter.
21. The computer-implemented method of claim 1 , further comprising:
generating a fourth series of data as a function of time using the first series of raw data comprising the plurality of subsets-of-interest, wherein the fourth series of data comprises a third data resolution for the plurality of subsets-of-interest and a fourth data resolution that is different from the first data resolution for data outside the plurality of subsets-of-interest.
22. The computer-implemented method of claim 1 , wherein
(i) a predetermined criteria comprises a joint predetermined criteria,
(ii) the joint predetermined criteria is used to identify the plurality of subsets-of-interest within the first series of raw data and to identify a second plurality of subsets-of-interest within the second series of raw data, and
(iii) the time intervals associated with the plurality of subsets-of-interest within the first series of raw data and time intervals associated with the second plurality of subsets-of-interest within the second series of raw data are used to identify the corresponding subsets-of-interest within the second series of raw data.