Nexen Energy ULC - community-dev.tibco.com€¦ · Tobi Adefidipe, Technical Analytics September 7,...

Tobi Adefidipe, Technical Analytics

September 7, 2016

Nexen Energy ULC

© Copyright 2000-2016 TIBCO Software Inc.

Agenda

About Me

Technical Analytics at Nexen

Examples of Deployed Solutions

Automating the SPEE Monograph 3 Workflow

Business Outcome

Future Direction


• Our Toolbox



• Production Monitoring

• Well History Matching

• Resource Evaluation

Examples of Deployed Solutions


Examples – Production Monitoring

Identify trends in Rates, Pressures

Flag potential operational issuesEmail Alerts

Spotfire User Interface

Plot Templates to investigate flagsAllows user inputs (Action taken,

Comments etc.)

Track Actions, Comments inputs

from SpotfireIron Python Script

Update R script with cleared

flags

Hourly Data from

Hadoop


Examples- Well History Matching

History Match Well Production Genetic Algorithm, 25+ variablesDeployed via Shiny – Web application framework for RScalable. Run History match for 1 well or 100 at the same time.

Multiple users Exact Same code/version

Store results of every history match in Hadoop

View Results in SpotfireResults from runs by other users


Evaluating Proved Area of a Resource Play –using the SPEE Monograph 3 Workflow


• Workflow published by Society of Petroleum Evaluation Engineers.

• Determine proved areal extent of a Resource Play.

• The wells must exhibit a repeatable statistical distribution of estimated ultimate

recovery (EURs).

• Mature plays – enough wells to observe statistical distribution.

• Shale Gas/Oil, Tight Gas/Oil, Coal Bed methane etc.

• Combines Statistical & Spatial Analysis of EURs

• SEC Requirement for Reserves Booking.

SPEE Monograph 3 Background



SPEE Monograph 3: Guidelines for the Practical Evaluation of Undeveloped Reserves in Resource Plays, Chapter 3, @Copyright 2010 by the Society of Petroleum Evaluation Engineers

1.Calculatepopulationstatistics- all

wells.

MeanP10 P90P10/P90 Ratio





wells.

2.RandomlyselectsetofAnchorwells.

Anchor Wells

Is the statistical distribution of Anchor Wells similar to the population?





wells.


3.CreateTestSetsatECRsawayfromanchorwells.

4.CalculateStatisticsforwellsintheECR.Isit

similartogeologicsubset? Distributionat1000mECR

Distributionat2000mECR





wells.



4.CalculateStatisticsforwellsintheECR.Isit

similartogeologicsubset?

Keep expanding the ECR until- Test Set Distribution ≠ Population Distribution- Not enough wells

Expanding Concentric Radis





wells.



4.CalculateStatisticsforwellsin theECR.Isit


5.Interpretresultstodefineprovedarea

Define proved area by creating “clipped polygons” from wells within the ECR around each anchor well




6.Define“ProvedAreas”formultipleiterations.

7.Overlayprovedareasfrommultipleiterations.

8.Decide“finalprovedarea”basedon

overlappingareas

Multiple Iterations


• Time

• Bias of Evaluator• Sampling Bias, Selecting Anchor wells• Confirmation Bias, Defining Proved Area

Why Automate?

Statistical Analysis Spatial Analysis


Methodology

• Statistical & Spatial Analysis in Open Source R

• Open Source. 6500+ packages.

• Using R for GIS/Spatial Analysis

• Integration with Spotfire –Deployed as a Data Function

• Scalable, Repeatable

• Visual Analytics / User Interface in Spotfire

• TERR Function to convert shapes to WKB objects.

• Add Map Layers, Other Shapes etc.

• Filter through different iterations/solutions.


Required Data

- 4 Column data table

- Well ID (Well Name, UWI, API Well Number etc.)

- EUR, EUR per foot (Mboe, Mboe/Ft)

- Well Location (Latitude, Longitude)

The EUR data used in the following example was randomly generated.


SPEE Monograph 3 in Spotfire


Input Data Validation

• Quick Insight into Data On a Map Visualization



• Filter Out Anomalies in the data



• EUR Distribution• Population Stats (P10, P90, Mean etc.)



• Cumulative Distribution Plot


Recommended Sample Size

• Recommended Sample Size


Recommended Sample Size

• Recommended Sample Size from Open Source R data function

• Fits a log normal distribution to the input data using P10,P90,P50

• rriskDistributions package in open source R1

• Randomly samples the distribution ~10000 times at different sample sizes.

• Plot of Confidence of Achieving Mean vs Sample Size.

1. Natalia Belgorodski, Matthias Greiner, Kristin Tolksdorf and Katharina Schueller (2015). rriskDistributions: Fitting Distributions to Given Data or Known Quantiles. R package version 2.1. https://CRAN.R-project.org/package= rriskDistributions


Input Options

• Number of Anchor Sets (Solutions)

• Number of Anchor Wells

• Minimum Sample Size for required confidence

• Click ‘Run’. ~ 5mins to run workflow for 14000+ wells.


Input Options


wells.



4.CalculateStatisticsforwellsin theECR.Isit


5.Interpretresultstodefineprovedarea

5 minutes to run 5 iterations of the Monograph 3 for 14000+ wells.


Results


Results – % Variance vs. Test Set


Results – Well Count vs. Test Set


Results – Summary Table


Results – Filter Through Multiple Solutions

• Scalable.

• Compare Results for different solutions.

• View Results for 1 or 100 solutions in the same

platform


Results – Proved Area Boundary


Results – Proved Area Boundary Polygon

• Convert ECRs to a WKB object. Rendered as a Geometry – TERR data function.

• Proved Area Boundary polygon is created by generating the Alpha Convex hull around wells inside the ECR.

• Alphahull Package in Open Source R1

1. Beatriz Pateiro-Lopez and Alberto Rodriguez-Casal. (2015). alphahull: Generalization of the Convex Hull of a Sample of Points in the Plane. R package version 2.0. https://CRAN.R-project.org/package=alphahull


Results – Overlapping Multiple Solutions.

• The Monograph 3 workflow recommends the final proved area be defined by at least 2 overlapping polygons.


Results – Final Proved Area

• Statistically Proved area using the SPEE Monograph 3 workflow.


• Evaluate our own resources

• Evaluate potential acquisitions

• Run a number of different scenarios and observe

the impact on proved area in real time.

• Better manage tight timelines & resources.

•Optimum Future Wells/Pads Placement to

maximize proved area.

Business Outcome


• Evolution of Technical Work

• Growing the analytics community at Nexen.

• 200 Spotfire Users, 10 R Users

• Oil Sands, Shale Gas, Global Exploration, HR, Finance etc.

Future Direction


Thank You!

Nexen Energy ULC - community-dev.tibco.com€¦ · Tobi Adefidipe, Technical Analytics September 7,...

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