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Beau Rollins
Expensive to drill wells when all goes to plan.
Drilling problems add costs throughout the well’s life.
The best scenario for minimizing costs and problems is to drill the plan as quickly as possible.
Planned
Map View Cross Section View
Best
Surface
Location
Surface
Location
vs. Actual
Drilling Subject Matter Experts
Ali Charlton
Tristan Arbus
Garrett Robberson
Real-Time Drilling Data Collection
Don Morrison
Amos Hall
BHA Details and Directional Surveys Data Collection
Beau Rollins
Measured Depth Azimuth Inclination
Position Measurement
Measured at discrete points. Tools cannot take
measurements while rotating.
Continuous Drilling Data
Bottom Hole Assembly (BHA) Details
𝜃1
BHA 1
BHA 2
𝜃2
MD AZI INC
Directional Survey
DP_1 DP_2 DP_3 DP_4 & DP_50 100 0 300 0 50 0 500
Map View Cross Section View
Planned vs. Actual
New Components Stabilizer
Measured Direction SurveyData is acquired from measurement tools at regular “survey stations” of 30’ to 90’
a part.
Interpolated Direction SurveyData is interpolated between measured “ survey stations” using the minimum curvature
method to populate data at 1’ intervals. This interpolation is performed by a software
called Compass.
SlidingSlow course corrections.
Train and
Validate
Score
The bit’s measured position, at any point, is its most likely position in an ellipse of uncertainty.
This uncertainty cumulatively grows as the bit moves laterally away from the point of origin.
Even after advanced processing, position errors persist.
Northing: 60’ x 20’ Easting 70’ x 20’
All the content on this slide is from this Halliburton presentation.
Can we predict our position before we take the next position measurement by predicting the change in azimuth and inclination of the bit based on drilling data and the details of the bottom hole assembly (BHA)?
Can we create a general model to apply to all actively drilling wells
Train and validate on one well. Score a second well.
Drilling parameters can account for most of the variance in the targets
Unmeasured rock properties could have impact
The model focuses on the relationship between drilling parameters and position
What are the factors that can predict the changes in
azimuth and inclination of the bit?
• Revolutions per Minute
• Rate of Penetration
• Toolface Data
• Differential Pressure
• Flow in
• Torque
• Weight on Bit
• Standpipe Pressure
• Hookload
• Block Position
• When are we Sliding?
• Is the Housing
Stabilized?
• Distance to Stabilizer
• Bend Setting (Degrees)
• Bend Type
• Bit Box to Bend Length
• Gamma Ray
Drilling Data
Bottom Hole
Assembly (BHA)
Details
Geologic Data
Targets
The objective is to model the change in inclination and azimuth based on the values the drilling data for
the current row.
This project will have three types of error:
1) Predicted Difference
2) Predicted Orientation
3) Predicted Spatial Position
Perfect correlation between these variables in this linear combination.Target: Dif Azi
Target: Dif Inc
70/30
Training/
Validation
partitioning
Only
horizontal
portion of
wellbore
Model competition and scoring of dif_azi
Model competition and scoring of dif_inc
I changed my approach after analyzing the model diagnostics from the previous modeling work to a
machine learning solution.
The four models:
Linear Regression
Decision Tree
Neural Network
Random Forest
Model Settings
Linear RegressionMain Effects Only
Model Selection: Stepwise
Selection Criteria: Validation Error
Decision TreeTarget Criteria: ProbF
Maximum Branch: 3
Maximum Depth: 6
Assessment Measure: Validation Error
Neural NetworkNetwork: Multi-Layer Perceptron
with 1 Hidden Layer with 5 Hidden Units
Optimization: Back Prop
Input Standardization: Standard Deviation
Max Iterations: 1000
Pre-Training: No
Selection Criteria: Validation Error
Random ForestMax Trees: 1000
Out of Bag Sampling: Proportional (40%)
Exhaustive: 5000
Max Depth: 50
Variable Importance: Loss Reduction
Selection Criteria: Validation Error
Model Competition Selection Statistic: Average Squared Error on Validation Set
Dif Azi Competition Dif Inc Competition
Training
Validation
X1 X2 X3 X4 X5 T1
2
3
4
5
6
7
8
9
n
.
.
2 variables are randomly chosen
60% of training data randomly chosen to build model
40% of training data randomly chosen as OOB (self validating)
Double validate with validation data
Recursive partitioning to maximize difference in average between splits
Ensemble scoring of new data
X2 X3
H
LL
Train1 OOB1X1 X4
L
Train2 OOB2
H
…
X2 X5
H
Trainn OOBn
L
Validation
1
2
3
n
X1 X2 X3 X4 X5 T T’
Find row in forest Average of Trees
Find row in forest Average of Trees
Find row in forest Average of Trees
Linear Regression
Neural Network
Decision Tree
Residual by Observation
Linear Regression
Neural Network
Decision Tree
Residual by Observation
Model Competition Selection Statistic: Average Squared Error on Validation Set
Dif Azi Competition Dif Inc Competition
Each champion model is scored
against a new well. The predictions
are saved and sent to Enterprise
Guide further analysis.
Orientation Error
Model Predictions Applied Orientation
Measured Orientation
Measured Orientation
Minimum Curvature Method Do it in SAS
Predictions
Apply to orientation measurement
Position Calculation
Map View Cross Section View
The green line is the model prediction for bit position. The blue dots are the actual measured position.
Model performance on a scored well.
Big dots are slide flags
The maximum error
in almost 1 mile of
drilling laterally is
+/- 5 feet. The final
East/West error at
TD is 3 feet.
Machine learning can produce useful predictive models for bit position that can be trained on wells and
deployed to other wells and still maintain predictive accuracy.
The Base SAS code can implement engineering equations which translate machine learned predictions
into orientation and position estimates.
The model should help our engineers minimize drilling costs and reduce problems throughout the
lifecycle of the well. If my company implemented this model, the reduction in lifecycle costs will
directly impact the bottom line of each well we drill moving forward.
The Directional Survey table
Variable 1: dif_AZI (Outcome). Change in Azimuth, where azimuth is the position of the bit relative to North.
Variable 2: dif_INC (Outcome). Change in Inclination, where inclination is the position of the bit relative to straight down.
The BHA table
Variable 3: Bend Setting (Predictor). The angle of the bend in the bottom hole assembly.
Variable 4: Bit Box to Bed Length (Predictor). The distance between the bend to the bit.
Variable 5: Bearing Housing Stabilized (Predictor). Flags whether the BHA is stabilized.
Variable 6: Distance to Stabilizer (Predictor). Distance between the stabilizer and the bend.
The Drilling Parameters table
Variable 7: HDEPTH (Predictor). The current depth of the hole.
Variable 8: TORQUE (Predictor). The current torque.
Variable 9: RPM (Predictor). The current revolutions per minute.
Variable 10: FLOW_IN (Predictor). The current flow in rate.
Variable 11: SPPA (Predictor). The current standpipe pressure.
Variable 12: DIFF_PRESS (Predictor). The current differential pressure.
Variable 13: HKLD (Predictor). The current hook load.
Variable 14: SWOB (Predictor). The current weight on bit.
Variable 15: MUD_VOLUME (Predictor). The current mud tank volume.
Variable 16: ROP (Predictor). The current rate of penetration.
Variable 17: BLKHT (Predictor). The current block height.
Variable 18: GTF (Predictor). The current reading from the gravity tool face.
Variable 19: MTF (Predictor). The current reading from the magnetic tool face.
Variable 20: PSUEDO_MSE (Predictor). The current value of the calculated mechanical specific energy.
Variable 21: Slide_v_ Rotate (Predictor). Flags whether the drill bit is rotating or sliding.
The model will be deployed in near real-time on incoming drilling data and directional surveys.
A time series transfer function or a linear regression model with serially correlated errors
seemed relevant initially, but there are a few concerns I have with the data and the model. My
concern with these approaches lies in the real-time deployment complications, and the inability
of the predictive modeler to dynamically account for: Missing data values due to transmission
errors; level shifts; and additive outliers. The data to be modeled is not as continuous as I had
previously believed. The interpolation method of inclination and azimuth resulted in repetitive
discrete numbers.
I chose a machine learning approach to solve the problem for their robustness in handling
missing values. I included a linear regression model in the modeling competitions out of
curiosity, however I anticipated the data would violate the assumptions of the linear model.
It is my opinion that predictive accuracy would trump parsimony in this business case. Akaike’s
Information Criteria should be the metric of choice when comparing models. However, tree
based models, such as random forests and decision trees, do not have this metric calculated on
them in Enterprise Miner. Minimizing average squared error on the validation set will be the
deciding metric.
R_Dif_Inc
R_Dif_Azi
Pred_azi_error
Pred_inc_error
EW_Error
NS_Error
TVD_Error