EMBRACING ANALYTICS ACROSS THE WELL LIFECYCLE GREG PALMER SR. INDUSTRY CONSULTANT

Transcription

1 EMBRACING ANALYTICS ACROSS THE WELL LIFECYCLE GREG PALMER SR. INDUSTRY CONSULTANT

2 THE HAPPIEST COUNTRY IN THE WORLD 1 Costa Rica 2 Vietnam 3 Colombia 4 Belize 5 El Salvador 6 Jamaica 7 Panama 8 Nicaragua 9 Venezuela 10 Guatemala 105 United States

3 THE HAPPIEST COUNTRY IN THE WORLD 1 Costa Rica 2 Vietnam 3 Colombia 4 Belize 5 El Salvador 6 Jamaica 7 Panama 8 Nicaragua 9 Venezuela 10 Guatemala 105 United States HPI = Experienced Well-Being x Life Expectancy Ecological Footprint

4 CONTINUUM OF ANALYTICS WHERE IS ANALYTICS TODAY?

5 VALUE CREATION THE POWER TO KNOW ANTICIPATE OPPORTUNITY

6 VALUE CREATION THE POWER TO KNOW EMPOWER ACTION

7 VALUE CREATION THE POWER TO KNOW DRIVE IMPACT

8 VALUE CREATION OUR POSITION ON ANALYTICS What s Important to SAS? Forrester Research Cites SAS as a Leader in Big Data Predictive Analytics Solutions Q1 2013

9 A PARADIGM SHIFT FROM ANALYSIS TO ANALYTICS ANALYSIS Common approach across technical domains

10 A PARADIGM SHIFT PARADIGM SHIFT ANALYTICS ANALYTICS The Sweet Spot Common approach across technical domains

11 SUMMARY DEFINITION DATA ANALYSIS VS. DATA ANALYTICS Analysis breaking down or mining data to gain valuable knowledge Analytics discovering meaningful patterns in data with reliance on statistics to anticipate future outcomes

12 OILFIELD LIFECYCLE ADDRESSING CUSTOMER CHALLENGES

13 OILFIELD LIFECYCLE ADDRESSING CUSTOMER CHALLENGES Mapping & Recon Prospect Generation Discovery Reservoir Delineation Construction Facilities Drilling & Completion Primary Production Maintenance HSE Enhanced Recovery

14 INDUSTRY TRENDS BIG DATA IS CHANGING OUR INDUSTRY The proof is in the pudding so to speak Sep 2012 Introduction of new SPE technical group, Petroleum Data Driven Analytics. Sub-group of the Digital Energy. First 6 months attracted over 400 members Collaborating on over 33 topics online.

15 OIL AND GAS CASES 15

16 OIL AND GAS CASES G&G, RESERVOIR CHARACTERIZATION PINEDALE CASE STUDY (SPE ) Situation Challenge Need to identify key production indicators across the anticline to address declining stimulation success; undesirable economics. Fragmented; unreliable sparse data across 211 wells; 2399 stages No accurate measure of poor/exceptional wells Unable to isolate significant variable impact 16 SAS Analytical Solution Time phased data integration aligned with all sources Key Performance indicators for accurate well scoring Deploy production models with probability distributions for potential outcomes at different categories of production (right time predictions) Value: 2-3% increased annual production, ~$1m savings per job

17 OIL AND GAS CASES UNCONVENTIONAL PLAY HYDRAULIC FRACTURING MAJOR U.S. INDEPENDENT OPERATOR Situation Challenge Unable to understand impact of proppant volume on production and isolate key variables in the hydraulic fracturing process and key variables within complex geology. Need to identify KPI s across 11,000 wells Stratify wells by various key characteristics Developed statistical clusters based on significant variables 17 SAS Analytical Solution Reduced cycle time for job planning Increased annual well production over lifetime Developed consistent and repeatable workflows Identified significant opportunities for fracture cost reduction Enabled modeling methodology for application in de-risking new plays Value: 30% cost reduction in proppant with considerable savings

18 OIL AND GAS CASES REDUCE UNPLANNED DOWNTIME REAL-TIME DECISION MAKING (SPE ) Development Production Enhancement Transmission Refining Situation Repeated unplanned downtime of steam powered turbines that powers air blowers for a Sulfur Recover Unit (SRU) - Desulfurization Challenge Triple redundancy couldn t stop production losses in SRU Root cause analysis inconclusive Ownership of problem spread across 4 departments 18 SAS Analytical Solution Data integration across ~35 data sources Enabled cross-departmental collaboration Analysis of past failures found commonality in failure signature Failure proved to be in configuration of process controls Monitoring 5 data sources provided 4 day advance warning Value: 20% increased throughput, 55% reduction in HSE risk

19 OIL AND GAS CASES EMPOWERING BUILDING BLOCKS WELL PROFILING ANALYSIS USING CLUSTERING Deep dive examples Statistics can be applied to improve well Production Performance so let s start with some statistics and linier equations

20 OIL AND GAS CASES EMPOWERING BUILDING BLOCKS WELL PROFILING ANALYSIS USING CLUSTERING Background 2009 launch of Saudi Aramco Engineering Solutions Center (ESC) Multi-disciplinary environment, integrated solutions Real-time data from field/plant to office.

21 OIL AND GAS CASES EMPOWERING BUILDING BLOCKS WELL PROFILING ANALYSIS USING CLUSTERING ESC Data Sources MS Access Production Rates Salinity Pressures (SWP) Completion Events Well Locations FWL Well Type Petrel PLT Data KH Values Log Spikes Lost Circulations Fractures (FMI) SAS Environment SAS Data Repository SAS EG SAS EM SAS JMP SAS Modeling and Statistical Analysis Tools for running exploratory analysis Outputs / Deliverables Clustering Results on Petrel Project Well Dissimilarity Matrices Cluster Results WCT Profile SAS PLT Reader List of Representative ( closest ) and ( farthest ) Wells

22 OIL AND GAS CASES EMPOWERING BUILDING BLOCKS WELL PROFILING ANALYSIS USING CLUSTERING Assessment Environment Creation Workshop with Aramco Deciding on problem and analysis scope Data Understanding Modeling Interpretation & Recommendations

23 OIL AND GAS CASES EMPOWERING BUILDING BLOCKS WELL PROFILING ANALYSIS USING CLUSTERING Assessment Environment Creation Data Understanding Modeling Interpretation & Recommendations Loading MS Access data base Session with Aramco ESC to understand the metadata and file structure Prepare data loading scripts Installing SAS on Aramco workstation(s) Importing data into SAS

24 OIL AND GAS CASES EMPOWERING BUILDING BLOCKS WELL PROFILING ANALYSIS USING CLUSTERING Assessment Environment Creation Data Understanding Modeling Interpretation & Recommendations Preparing data model for the analysis Create and transform data Prepare analysis environment Populate basic summary statistics Exploratory analysis Histograms, scatter plots on production curves

25 OIL AND GAS CASES EMPOWERING BUILDING BLOCKS WELL PROFILING ANALYSIS USING CLUSTERING Assessment Environment Creation Data Understanding Modeling Interpretation & Recommendations Finalizing analytical dataset: Deriving variables that are going to be used in the modeling Eliminating time dimension and integrating into the indicator attributes Building initial clustering models Identifying clusters Assessment of the cluster results Generating report and clustering output

26 OIL AND GAS CASES EMPOWERING BUILDING BLOCKS WELL PROFILING ANALYSIS USING CLUSTERING Water Cut Clusters & Profiles Time Window 1 ( - Jan 96) Time Window 2 (Jan 96 Oct 03) Time Wndow 3 (Oct 03 - ) Low water production rate V wells distant to the FWL Mostly dry wells ¼ being matrix producer V wells having significantly high water production (relatively) Mostly strataforms (1/2) and matrix producers(4 0%) Low water production rates Dry wells (55%) Fracture signature seen for 17% 1/5 strataforms Biggest cluster of wells in 2 nd Time Window Similar Gas prod to 2-1 Equally likely distribution of strataforms and matrix producers Mix of V and H wells having high water production levels Mostly strataforms ~1/3matrix producers High KH values Significant water production High WCT levels ~1/3 showing Almost half being strataforms fracture signatures (Few observations) Highest Gas oil ratio Largest cluster, all H wells Most of the Lost circ. Identified for those clusters Majority dry (81%) Low water production rates H wells Most likeliy to be a strataform (70%) Small number of new vertical wells, practically there is no water nor prod oil yet High salinity Closeness to FWL High Water Production Highest WCT levels Horizontal wells ~30% showing fracture signatures Complex WCT behavior Highest water production rate Low salinity ~30% showing fracture signatures 40% strataforms, no matrix formation identified

27 OIL AND GAS CASES EMPOWERING BUILDING BLOCKS WELL PROFILING ANALYSIS USING CLUSTERING The analysis for each time-window reveals 2 clusters in time-window 1, 4 clusters in timewindow 2 and 5 clusters in time-window 3.

28 OIL AND GAS CASES EMPOWERING BUILDING BLOCKS WELL PROFILING ANALYSIS USING CLUSTERING Assessment Environment Creation Data Understanding Modeling Interpretation & Recommendations Assessing the results with the team Conclusion & Closure

29 OIL AND GAS CASES EMPOWERING BUILDING BLOCKS WELL PROFILING ANALYSIS USING CLUSTERING * Bubble sizes reflect number of wells in the cluster There are 40 wells having FMI logs and their fracture characteristics have been identified. Unfortunately FMI is not available for the rest of the wells 25% Distribution of Known Fractures in Clusters (%) Clustering results have been analyzed with respect to the distribution of the fractures that are known Cluster # of Wells # of Known Fractures % in the cluster % in the whole set of fractures % 0% % 0% % 12% % 12% % 10% % 12% 15% 5% % 7% % 30% % 0% -5% % 17% % 50% Relatively high number of fractures It is known through FMI that the half of the wells are crossing through a fracture

30 OIL AND GAS CASES EMPOWERING BUILDING BLOCKS WELL PROFILING ANALYSIS USING CLUSTERING Number of observations 40 Distribution of Known Fractures in Clusters (%) Lost Circulations Log Spikes Strong correlation of lost circulations and log spikes Significant difference in observations for the 3-1 cluster

31 OILFIELD LIFECYCLE ADDRESSING CUSTOMER CHALLENGES Mapping & Recon Prospect Generation Discovery Reservoir Delineation Construction Facilities Drilling & Completion Primary Production Maintenance HSE Enhanced Recovery

32 SPE CONTRIBUTIONS MAKING ANALYTICS MAINSTREAM Title Number Description Customer Mitigating Risk - from Geological Understanding Tight Gas Well Performance Evaluation With Neural Network SPE Reduce cycle time to achieve improved development plans Aramco SPE Identify opportunities to reduce stages and increase margins Shell Real-time Decision Making In Operated Asset Management SPE Reduce unplanned shutdowns increase reliability and profitability Aramco Increased Upstream Asset NPV With Forecasting, Prediction, and Operational Plan Adaptation in Real Time SPE Collaborative real-time event management and plan adaptation using cross functional Operation Center planning the right actions for the right situations and proactively preventing disruptions by predicting early performance deviations. SAS Analysis of Production History in Mature Fields SPE Optimize production and work over through statistical clustering of key well characteristics Total Drilling Optimization in Unconventional Tight Gas SPE Increase certainty in identification of stimulation approaches to maximize production SAS Automating Well Performance Monitoring of Real Time Data SPE Create highly accurate predictive models utilizing data across the enterprise Aramco Exploratory Data Analysis in Reservoir Characterization SPE Improve well management decisions by exploiting proven data exploration techniques KEC

33 GOING FORWARD E&P VALUE CHAIN Immersive Visualization - 3D rendering subsurface data integrated with SAS Analytics Platform and Data Management SAS and OSIsoft PI Data Integration Data Federation / Data Governance Real-Time Workflows Real-time Drilling Optimization Oilfield Performance Forecasting

34 IN CONCLUSION SEAMLESS SOLUTION FRAMEWORK Mapping & Recon Prospect Generation Discovery Reservoir Delineation Construction Facilities Drilling & Completion Primary Production Maintenance HSE Enhanced Recovery Embracing Analytics across the Well Lifecycle

35 IN CONCLUSION SEAMLESS SOLUTION FRAMEWORK Mapping & Recon Prospect Generation Discovery Reservoir Delineation Construction Facilities Drilling & Completion Primary Production Maintenance HSE Enhanced Recovery Embracing Analytics across the Well Lifecycle

36 SAS SOLUTIONS FOR OIL & GAS