Big data analytics for production: integrating the reservoir management disciplines

Transcription

1 Big data analytics for production: integrating the reservoir management disciplines Using Analytics to Improve Production Duncan Irving, Oil and Gas Practice Lead Aberdeen, 23 rd September 2014

2 Agenda How to know big from thinking big Talk contents What is analytics at scale? Bedding in a few buzz words How is it provisioned? A high-level view of the architecture What is the value? Some case studies Teradata

3 What is analytics at scale? There is a growing requirement to take: a historical perspective time series as long as possible an operationalised attitude generate right-time insights an interdisciplinary view with insights from all components a robust foundation process as much data as possible to drive a decision and an action

4 Data Lake

5 or a reservoir, more like

6 and so many lakes

7 but with the passing of time

8 A data-driven industry

9 We keep our data close to the action

10 A proliferation of data marts

11 Information supergarden

12 Bringing it all together Data outhouse ODS Data marts Ingest Analytics Views and visualisations

13 Why integrate? Cartoonist Hugh MacLeod nails it with this cartoon: There is a world of difference in the workplace between knowing facts, and knowing how those facts fit together. Even more important is knowing what to do about it. 1 Source: 1 Marc Cenedella, Founder The Ladders Integration Relationships Context Value

14 Case Study Discovery Analytics for Drilling 14

15 Drilling Story It is expensive to drill. Drilling involves a large number of unknowns especially in Exploration phase and a large number or parameters, mud weight, torque, weight on bit, and so on. Adjusting these parameters while drilling is a modern practice because of downhole drilling measurements

16 Drilling Efficiency/Safety Stuck Pipe = cost Why stuck? Geology (link) e.g. swelling shales Rock properties e.g. weak rocks Deviation/deviated wells Bit type Mud type WBM vs OBM Other If we can analyse the conditions causing stuck pipe we can reduce the risk/cost

17 Borehole Conditions Poor borehole means: Potential stuck pipe More wiper trips Cementing problems Logging problems Stuck, sticking tools, more wiper trips Poor data quality, poor completion decisions

18 Bad Hole Results Sonic wrongpoor correlation with seismic Porosity wrong Hydrocarbons wrong Expensive testing decisions could be made on these results. Incorrect seismic processing would be done on this data.

19 Bad Hole Example Single Well The completion log gives no clues to the problems encountered.

20 Data Links A lot of these connections are routine, check shots and seismic, fluids and pressures. Some of this data is used in combination in reservoir studies, seismic, well logs, formation tops, pressures, fluids, core data. However these are single instances, single wells or a field study.

21 Data Not Linked A lot of data types are not-linked or only linked occasionally. Why? Are all links equal or are some ridiculous? Sometimes new techniques are found by linking diverse data types for example - Seismic to Fluids is AVO - Seismic to pressures is overpressured zones

22 Combining Big Data Techniques & RDBMS Oil Samples

23 Initial Data Load This view was built using drilling parameters and well logs directly with Aster technology and without the need for data preparation, modelling and indexation

24 Data Visualisation Multi Well Visualisation of many parameters simultaneously

25 Data Analysis Analysis of the data gives correlations and probabilities.

26 Case Study Reservoir Monitoring for Better Intervention Planning 26

27 4D Seismic seeing what happened

28 Traditional Marine Seismic Surveys Planning 1 year Acquisition 1 month Processing 6+ months Interpretation 3-6 months Average 2 years between snapshots of the reservoir

29 Permanent Reservoir Monitoring (PRM) Operationalizing the workflow With a fixed seabed receiver array: Simpler source vessel Cheaper per survey More weather independent Receiver geometry the same Surveys are more repeatable Faster processing turnaround Can do more frequent surveys

30 PRM: Shortens the time frame Planning 1 month Acquisition 3 weeks Processing <3 months Interpretation <1 month New survey at least every 6 months Decision making on the timescale of interventions Need a much more streamlined process for receiving new data and interpreting it

31 And brings so much data Base Monitor1 Monitor2 Monitor3. times hift times hift times hift times hift times hift times hift times hift. Difference base to TS Mon 1 Difference base to TS Mon 2 Difference Mon 1 to TS Mon 2 So many surveys, time shifts, differences between pairs, attributes to look at. And the governance..? Attribute maps on horizon 1 and horizon 2

32 Learning from other industries

33 We made a Reservoir Data Warehouse! We store detailed subsurface data in an MPP Analytical database We integrate it in space and time as well as logical relationships And users can visualise detailed data and analysis, calculated onthe-fly

34 Example 1: Repeatability Analysis Good image of 4D effect requires seismic image taken from the same position. NRMS is measure for survey repeatability. NRMS map Areas with bad survey repeatability Good image of a 4D effect Bad image of a 4D effect

35 NRMS Offset devaition (m) Example 1: Repeatability Analysis Colored by base-monitor source + receiver deviation (meters) Colored by basemonitor azimuth deviation (degree) Bad ood Bad repeatability Not great repeat - ability OK repeatability Base receiver station nr Offset x Azimuth deviation Source/receiver positions repeated well Source/receiver angle repeated well, distance not Source/receiver distance repeated well, angle not Source/Receiver (base) Source/Receiver (monitor) Classification: Internal 35

36 Example 2: Subsurface Analytics Repeatability (NRMS) Time shift Pressure difference

37 Example 2: Subsurface Analytics SQL query and result set (best correlation on top) Time Shift vs Pressure Further down the list. Time Shift vs Water Saturation

38 Example 2: Subsurface Analytics Clear correlation No correlation

39 Results 4D workflows not fully supported by today s tools Explaining 4D effects requires other data Identifying artefacts of processing or acquisition Identifying events that correlate Giving confidence to data-driven decisions

40 Future Directions Future Directions 41

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44 Time Series Analysis of Log Curves Correlated GR signatures suggest similar geology Measurement depth is warped to match 290 GR curves from 121 wells. Distance between warped curves is a measure of curve similarity. Completed to test viability of using advanced statistics to analyze log curves.

45 and beyond wells Sensor data stability of sensor arrays (e.g. passive seismic R OPTICAL TRACKER RIGHT ENG START L OPTICAL TRACKE R listen to your assets: what is changing? L HANGR BRG VIB/TEMP SNSR 4 L HANGR BRG VIB/TEMP SNSR 1 semi-automated QC during seismic acquisition integrate with relational data for RCA and causality L EXHAUST DFTR FAIL PFCS FAIL RESET R HANGR BRG VIB/TEMP SNSR 1 GENERAT OR 2 (NIU 2)

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