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Regional Saltwater Disposal Facility Planning
Utilizing Data Analytic Methods
Chad E. Kronkosky, P.E.
CEK Engineering LLC, Texas Tech University
Amin Ettehadtavakkol, Ph.D.
Texas Tech University
Outline
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Introduction
Data-Driven Predictive Analytics
State of the Saltwater Disposal Industry
Spatial Optimization of Saltwater Disposal Facility Sites
Future Research / Software Development
Summary
What Exactly is Data Analytics? Why is it Important to the Saltwater
Disposal Industry?
INTRODUCTION
What is Data Analytics?
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Mathematics & Statistics
Information Theory
Computer Science
Visualization
Data Mining
Why is Data Analytics Important to the SWD Industry
• The SWD Industry is an inherently complex; significant opportunities for capital
optimization!
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If we build it they will come strategies fail – Not knowing thy need!
We can install a facility anywhere, right? – Not knowing thy geology/regulations!
We can service all your disposal needs – Not knowing thy customer or thy self!
• Applying data analytic methodologies allows for rapid answers/response
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Specific targeting of developing plays, customers… with a detailed understanding
current SWD asset picture – We now know thy need!
Geologic/Regulatory spatial models can give us an understanding of expected injectivity
rates, and allowable reservoirs – We now know thy geology/regulations!
Rapid identification of optimal Customer/SWD asset pairing; does if make since to take
on this account – We now know thy Customer and thy self!
Application of Data-Driven Predictive Analytics Workflow
DATA-DRIVEN PREDICTIVE
ANALYTICS
SWD DDPA Workflow (Asset Location & Transportation Optimization)
Data Collection & Database
Construction
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TRRC UIC Database
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IHS U.S Well Data
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Lease/Well Spatial Locations
IHS U.S. Production Data
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Commercial SWD
Identification
Detailed Wellbore Info.
Injectivity Info.
Water Production Estimates
U.S. Census TIGER Roads
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GIS Layer for Potential Facility
Sites (i.e. next to US/FM
roadways or paved surfaces)
SWD DDPA Workflow (Asset Location & Transportation Optimization)
Data Preparation
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ETL & SQL Queries built to
validate information; typically
performed by IT professionals
Subject Matter Experts (SME)
help IT establish business rules
and validation parameters
Knowledge provided by SME’s
helps alleviate spurious
conclusions
SWD DDPA Workflow (Asset Location & Transportation Optimization)
Exploratory Analysis
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This involves the core technical
knowledge of SME (engineering
geology, financial)
Incorporates gathered data into
integrated descriptive/predictive
analyses (classical statistics,
data mining, artificial
intelligence/machine learning,
optimization, GIS)
IT and SME all contribute to
develop models and workflow
modules which seek to provide
outputs which aid the business
process
SWD DDPA Workflow (Asset Location & Transportation Optimization)
Predictive Modeling
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Correlation structures are
obtained from the exploratory
analysis.
Quantitative/Qualitative outputs
identifying uncertainty/sensitivity
are developed.
Data analytic tools (regression,
optimization, etc.) are
incorporated to maximize the
use of disparate data sources.
An analysis of regional transportation patterns
STATE OF THE SWD INDUSTRY
State of the SWD Industry (at least in Texas)
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By the Numbers (Texas)
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Produced/Flowback Water Represents a Substantial Portion of the Lifetime Operating Expense
Incurred by Oil and Gas Assets
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In 2013, 7.15 Billion bbls of produced water was disposed of in the State of Texas (4 bbl produced water for
every 1 BOE)
If only 30% of this volume was transported by trucking; 16.5 Million vehicle trips would be required.
Based on preliminary research; each haul requires ~ 50 miles of driving; 825 Million Miles of driving across the
state
Disposal cost can be minimized through the minimization of disposal hauling distances and driver standby
charges
We observe excessive hauling distances (> 25 miles), with regular frequency, taking place in regards to
commercial SWD
The Following as a Transportation Pattern Case Study for Andrews County, TX (January 2013)
Andrews County, TX (01/2013)
SWD Lease Locations
and Their Respective SWD Facilities
Andrews County, TX (01/2013)
SWD Lease Routing
and Their Respective SWD Facilities
Andrews County, TX (01/2013)
SWD Lease Routing
for SWD’s in the County
Andrews County, TX (01/2013)
SWD Facilities Lease Routing Distances
A heuristic random search algorithm for gridded networks
SPATIAL OPTIMIZATION OF
SWD FACILITY SITES
How Do We Spatially Optimize SWD Facility Site Locations
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Project Delineation
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Import Datasets
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Incorporate production data, GIS dataset into a geospatial database
Utilize a routing algorithm to perform distance/time estimates (we used Google Map Direction API)
Subdivide and Aggregate
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Specify spatial and temporal boundaries and constraints (e.g. maximum facility disposal rates, maximum
hauling distance, etc.)
These boundaries can be at the lease/field scale, or larger regional scales, county/basin
Subdivide the project extents into a gridded network
Aggregate produced/injected water volumes by temporal range for each grid cell
Determine Net Produced Water for each grid cell (NPW = Produced – Injected)
Remove and Filter
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Filter and remove from the gridded network cells with negative NWP. These cells will likely represent areas
with significant secondary/tertiary recovery operations
How Do We Spatially Optimize SWD Facility Site Locations
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Specify Potential SWD Facility Site Locations
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Sample Candidate SWD Site Locations
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Calculate a distance matrix to grid cell centers. This matrix is used to collect nearest neighbor grid volumes
(until volume constraint is reached) for each Candidate Site Location.
Grid Cell Center to Candidate SWD Facility Sites Routing
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Sample a random roadway point within each grid cell (if one exists); these points will represent Candidate
SWD Facility Sites within the project extents.
An order of precedence can be implemented (i.e. 1. State Hwy, 2. FM, 3. US)
Grid Cell Center Distance Matrix
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In almost every instance SWL Facility Sites will be located near or at roadway ROW’s
GIS roadway layers can be utilized to establish roadway points for Candidate SWD Facility Sites
Roadway criteria can be incorporated (i.e. only US/State Hwy/FM roadways)
Calculate routing estimates for each grid cell to each candidate SWD Facility Site
Random Search for Global Optimum (not global but close enough!)
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Randomly sample a grid cell; collect grid cell neighbors until SWD Facility volume constraint met.
Search for nearest Candidate SWD Facility Site; remove the NPW grid cells and Facility site from there
respective lists
Repeat process until all NWP grid cells are removed
Applications to other industry problems?
FUTURE RESEARCH &
SOFTWARE DEVELOPMENT
Future Research and Software Development
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Adapt Current Algorithm to Handle Generalized Cases
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Include the ability to handle SWD pipelines (minimum distance threshold)
Mileage and duration estimates are based on the grid center of the randomly sampled grid cell. This
should be based on actual the lease/well centroids (and weighted by number of trips)
Produced water being disposed of at existing facilities is not accounted for in the algorithm. A cost
function should be included (e.g. a greedy collection strategy based on reduced hauling
distance/duration)
Develop a stochastic/Monte Carlo simulator to estimate future SWD demands within project extents
Develop a WebApp SAAS Platform in the Next 18-24 Months
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Proof of concept/prototype spring/summer 2017
Wrapping it up!
SUMMARY
Summary / Acknowledgements
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The SWD Industry is complex!
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Supply/Demand issues; Offset Competition; Injectivity/Regulatory (e.g. Induced Seismicity)
Apply DDPA methodologies, we believe significant improvements can be achieved in regards to
optimizing hauling distance/durations (20-30% reductions appear to achievable).
Be on the look out for our open source project on CRAN Spring/Summer 2017
• This research would not have be possible without the “R” Statistical Software
Community!
• Thank you for your time!
Regional Saltwater Disposal Facility Planning
Utilizing Data Analytic Methods
Chad E. Kronkosky, P.E.
CEK Engineering LLC, Texas Tech University
Amin Ettehadtavakkol, Ph.D.
Texas Tech University