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