Oilfield Data Science and Machine Learning/AI APRIL 13 TH, 2018 DAVID PEACH PRODUCTION ENGINEER

Transcription

1 Oilfield Data Science and Machine Learning/AI APRIL 13 TH, 2018 DAVID PEACH PRODUCTION ENGINEER 1

2 Why do we care about BIG DATA The importance of data in today s world What is big data? How can we use it? When do we use it? Problems/limitations? Big data can be a massive advantage in the oil and gas business, but can also be detrimental if not implemented properly. 2

3 Data Science What is data science? Who can do it? How do we properly implement it into oilfield applications? 3

4 Start with the Basics - Excel 4

5 Start with the Basics - Excel Manipulate, filter, and check data 5

6 Start with the Basics - Excel Chart Pump Intake Pressure Frequency Motor Temperature /21/ /11/ /31/2017 1/20/2018 2/9/2018 3/1/2018 3/21/2018 4/10/ /21/ /11/ /31/2017 1/20/2018 2/9/2018 3/1/2018 3/21/2018 4/10/2018 6

7 One Step Further Data Analytics 7

8 Data Analytics/Machine Learning - Options Excel Spotfire Microsoft Power BI Tableau Looker Domo Azure Orange etc 8

9 Data Analytics - Options Can t we just do all of this in Excel? a) Yes b) No c) Depends on how much spare time you have d) Your boss likes it e) All of the above 9

10 Data Analytics Why We Need more than Excel Specific data analytics software is designed to: Handle large datasets Share reports and ideas Be more flexible Overcome file size limitations Provide dashboard capabilities Enable better visualizations Easier manipulation of data.the list goes on 10

11 Data Analytics Here s what we can do Easily pull data from multiple sources Databases, programs, Excel sheets, cloud server, and more Assemble this data set in an easy to view and manipulate format Run non-destructive calculations and statistical analysis in either new columns or in individual visualizations Present live data and dashboards to piers 23 Data sources in one project! 11

12 Supervised Machine Learning/AI Let computing power and advanced algorithms do the work for you Unsupervised 12

13 Machine Learning The basic outline Determine desired outcome of project Analyze and select appropriate data Manipulate and clean data as much as possible Load data into machine learning program/service Further manipulate and clean data within program Select appropriate inputs from data and select an algorithm that matches your data and project goals Split data Some data will be used to train the model, some will be used to validate and score the model Run the model, view results Load new data into trained model, run, and view predicted results 13

14 Machine Learning One Step Further Automate the process! Feed live data through database or cloud service as input Data will then run through trained model Predictions can then be filtered to a web service or back to equipment to make changes on the fly Those results are then fed back into the model, further training it with the new data 14

15 Machine Learning Things to Consider Not everything is what it seems Data is dirty and poorly formatted no matter the source The same algorithm won t work every time Be mindful of small datasets Know that these predictions do get better with more data and training, but they are still just predictions Not every set of big data has an application in machine learning 15

16 Artificial Intelligence Use what we know about data science and machine learning to make AI do the work for us! 16

17 Artificial Intelligence In the Oilfield Many current applications already in place Plunger lift Gas lift/gapl Nearly unlimited potential future applications ESP control Facility control Monitoring and safety 17

18 Big Data and Artificial Intelligence How Big is it? 18

19 Examples in Action Data Science - Production Multiple data sources in one view 23 total includes 4 Excel sheets and 19 connections to internal databases All connections linked together enabling single page analysis Internal calculations possible across data sources 19

20 Examples in Action Data Science - Project Tracking 20

21 Examples in Action Data Science Failure Tracking Failure tracking made simple. All automated from internal system. R code to build tables. 21

22 Data Science What has it done for SM? Streamline workflows Eliminate manual tasks Eliminate many unnecessary spreadsheets Allow non-power users to easily consume data Powerful statistical analysis made easy Saves time in day to day work = SAVE MONEY 22

23 Examples in Action Machine Learning ESP Power Calculate power requirements for future ESP wells, along with monitoring costs on currently producing wells. Load in production data and ESP drive data. Calculate $/BOE and $/BTF per well. Run historical data through machine learning process to build a base model (training model). Load in type cure data for new wells. Use trained model to predict future power requirements and costs. Take predicted values back into data analytics tool to view and consume. 23

24 Examples in Action Machine Learning ESP Power 24

25 Examples in Action Machine Learning ESP Power 25

26 Examples in Action Machine Learning ESP Power Predicted values from experiment Can output to web service or Excel. Now load back into main analytics project. We now have values for future ESP wells kw kwhr Up Hours Total Fluid GOR GLR BOE WC (%) kw Predicted

27 Examples in Action Machine Learning ESP Power Why use this over other methods? We can apply to each individual well Power requirement based on a wide range of variables No one size fits all approach Process is fast and relatively easy Can be implemented immediately with confidence 27

28 Artificial Intelligence SM Energy South Texas (Eagle Ford) Currently utilizing machine learning and AI to control plunger lifted wells Project began with monitoring well performance based on plunger arrivals and departures A model was trained and continues to be updated constantly as new data comes in This trained model then tells the plunger when to drop based on predicted parameters Wells are treated individually and automatically controlled on an individual bases Production has improved on plunger lift wells using this method 28

29 Artificial Intelligence - Upcoming Similar to the plunger lift AI, there are some exciting projects currently in the development phase at SM. **Note: The following is currently in development so data cannot be displayed or shared. 29

30 Artificial Intelligence - Upcoming Basic premise of project: Well and equipment data is gathered Data run through machine learning algorithm to build base model New data continues to update and improve base model with live data Trained model then predicts optimal equipment settings These settings are implemented immediately to improve well performance This project could drastically impact SM well performance, and potentially be a game changer for a large array of oilfield applications. 30

31 What I hope you got from this presentation A basic understanding of big data, data analytics, machine learning, and artificial intelligence How important it is to us as engineers and scientists and why we should care How important it is to the oilfield currently, with more and more focus in the future The shift from traditional methods to big data How much fun it can be! 31

32 Questions? 32