Expensive to drill wells when all goes to plan. Drilling problems add costs throughout the well s life.

Transcription

1 Beau Rollins

2 Map View Cross Section View Expensive to drill wells when all goes to plan. Surface Location 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. Surface Location Best Planned vs. Actual

3 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

4 Position Measurement Measured at discrete points. Tools cannot take measurements while rotating. Measured Depth Azimuth Inclination Map View Cross Section View Continuous Drilling Data DP_1 DP_2 DP_3 DP_4 & DP_ Planned vs. Actual Directional Survey MD AZI INC BHA 1 Bottom Hole Assembly (BHA) Details θ 1 BHA 2 θ 2 New Components Stabilizer

5 Measured Direction Survey Data is acquired from measurement tools at regular survey stations of 30 to 90 a part. Interpolated Direction Survey Data 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. Sliding Slow course corrections. Train and Validate Score

6 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.

7 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

8 What are the factors that can predict the changes in azimuth and inclination of the bit? Drilling Data Bottom Hole Assembly (BHA) Details Geologic Data 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

9 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

10 Target: Dif Azi Perfect correlation between these variables in this linear combination.

11 Target: Dif Inc

12 I changed my approach after analyzing the model diagnostics from the previous modeling work to a machine learning solution. Only horizontal portion of wellbore 70/30 Training/ Validation partitioning Model competition and scoring of dif_azi Model competition and scoring of dif_inc

13 The four models: Linear Regression Decision Tree Neural Network Random Forest Linear Regression Main Effects Only Model Selection: Stepwise Selection Criteria: Validation Error Model Settings Decision Tree Target Criteria: ProbF Maximum Branch: 3 Maximum Depth: 6 Assessment Measure: Validation Error Neural Network Network: 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 Forest Max Trees: 1000 Out of Bag Sampling: Proportional (40%) Exhaustive: 5000 Max Depth: 50 Variable Importance: Loss Reduction Selection Criteria: Validation Error

14 Model Competition Selection Statistic: Average Squared Error on Validation Set Dif Azi Competition Dif Inc Competition

15 n.. X1 X2 X3 X4 X5 T Training H Train 1 OOB 1 X2 X3 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 Train 2 OOB 2 X1 X4 L Train n OOB n X2 X5 H LL H L Validation X1 X2 X3 X4 X5 T T Validation Find row in forest Find row in forest Find row in forest n Average of Trees Average of Trees Average of Trees

16 Residual by Observation Linear Regression Neural Network Decision Tree

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18 Residual by Observation Linear Regression Neural Network Decision Tree

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20 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.

21 Model Predictions Applied Orientation Measured Orientation Measured Orientation Orientation Error

22 Minimum Curvature Method Do it in SAS

23 Predictions Position Calculation Apply to orientation measurement

24 Model performance on a scored well. The green line is the model prediction for bit position. The blue dots are the actual measured position. Map View Cross Section View

25 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.

26 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.

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28 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.

29 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.

30 R_Dif_Azi R_Dif_Inc

31 Pred_azi_error Pred_inc_error

32 EW_Error NS_Error TVD_Error