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TRANSCRIPT
Net Present Value Prediction Using Artificial Intelligence
Group 4 Tongxin Zhang Ahmed Aljeshi Lawrence Bustos Olufemi Hussain Mesut Yildiz (TL)
Outline
○ Introduction
○ Neural Network
○ Data
○ Net Present Value
○ Softwares
○ Results
○ Recommendations
Introduction
○ Objective: Create an alternative method to predict
Net Present Value of a well using Artificial Intelligence
○ Net present value (NPV) shows economic
feasibility
Neural Network- SPE Papers
○ Solution: • Predicting Oil and Gas Spot Prices using Chaos
Time Series Analysis and Fuzzy Neural Network Model
• Forecasting, Sensitivity and Economic Analysis of
Hydrocarbon Production from Shale Plays Using Artificial Intelligence & Data Mining
Neural Network- Supervised
Neural Network- Attributes
1 Hidden Layer 6, 12, 18 Neurons Vs 100 Neurons
Finding Data
○ No real example of both production and
economics.
○ Used Pleito oilfield data
○ Traditional NPV Calculation
Pleito Field- Location
○ Operator: Vintage Production California LLC
Pleito Well- 48-25 (DOGGR)
Production: 1977-2013 (Active) Cumulative Gas: 612,540 Average Oil Rate: 3,508 Barrels GOR: 400.6 SCF/Barrel Cumulative Oil: 1,550,365 Ave Water Cut: 40%
Data Used- Actual Excel
○ Real production data from a well in Pleito
oilfield in California
○ Calculate NPV - assumptions
• CAPEX, OPEX
• Tax, royalty, interest rates
• Oil and gas prices are actual
NPV Calculation - Equations
○ Gross revenue = produced volumes x unit price
○ Net revenue = (Gross Revenue) – (Royalty) ○ Gross Income= (Net Revenue) – (OPEX) ○ Taxable Income= (Gross Income) –
(Depreciated/Amortized CAPEX) ○ Net Cash= (Gross Income) – (Tax) ○ Net Present Value =
Softwares - Matlab
6 Neurons 12 Neurons 18 Neurons
Neural Network Toolbox
Softwares - Matlab
○ Training Function:
Levenberg-Marquardt backpropagation
○ Activation Function:
Hyperbolic tangent sigmoid function
Graph and Symbol
Matlab Results
Validation Results Comparison(2008-2010)
Matlab Results
Validation Results Comparison(2008-2010)
Matlab Results
Prediction Results Comparison(2011-2013)
Matlab Results
Prediction Results Comparison(2011-2013)
Matlab Results
Training Validation and Prediction Results Comparison (1977-2013)
Matlab Results
Regression Comparison
6 Neurons 12 Neurons 18 Neurons
R=0.9738 R=0.9811 R=0.9911
NEURAL NETWORK SOFTWARE
PRESENT VALUE DATA
Actual Avg. Present Value So3ware Avg. Present Value
TRAINING
1977 24385.80478 24385.80478 1978 16303.96248 16303.96248 1979 21513.6047 21513.6047 1980 31047.59555 31047.59555 1981 31632.88675 31632.88675 1982 52263.0045 52263.0045 1983 40537.34312 40537.34312 . . . . . . . . . . . .
2004 6959.993386 6959.993386 2005 8277.689996 8277.689996 2006 7952.507929 7952.507929 2007 8226.340719 8226.340719
VALIDATION 2008 9301.399161 9301.399161 2009 2609.61142 2609.61142 2010 4472.392672 4472.392672
FORECASTING 2011 4910.086479
2012 1905.528339
2013 2270.595203
VALIDATION PRESENT VALUE
Actual NPV Predicted NPV Error
2011
Jan-‐11 6254.210998 3968.980439 0.365 Feb-‐11 4210.616433 3047.597601 0.276 Mar-‐11 6678.912587 4009.373653 0.400 Apr-‐11 5752.610198 3124.110503 0.457 May-‐11 5967.42932 2654.518389 0.555 Jun-‐11 5754.404235 2144.55317 0.627 Jul-‐11 5938.082057 1918.349572 0.677 Aug-‐11 5341.303176 2459.746053 0.539 Sep-‐11 5010.045412 3011.531335 0.399 Oct-‐11 2072.744265 4279.799643 1.065 Nov-‐11 2739.046112 5254.787882 0.918 Dec-‐11 3201.632958 5734.639865 0.791
2012
Jan-‐12 1816.91691 5591.116417 2.077 Feb-‐12 166.1086102 5763.480279 33.697 Mar-‐12 1447.199972 4964.070376 2.430 Apr-‐12 2269.753447 4567.958398 1.013 May-‐12 1950.825323 3718.48113 0.906 Jun-‐12 1947.415668 2728.736722 0.401 Jul-‐12 2188.639601 3365.730407 0.538 Aug-‐12 2051.109089 2983.073937 0.454 Sep-‐12 2325.984921 3162.876587 0.360 Oct-‐12 2480.888188 2131.712137 0.141 Nov-‐12 2121.758724 1956.521861 0.078 Dec-‐12 2099.739618 2215.269226 0.055
2013
Jan-‐13 2122.665132 2033.839355 0.042 Feb-‐13 2215.965402 2021.75256 0.088 Mar-‐13 2301.997505 1947.024223 0.154 Apr-‐13 2204.113861 1707.351853 0.225 May-‐13 2151.571775 2324.322663 0.080 Jun-‐13 1887.566247 2121.71863 0.124 Jul-‐13 2601.538906 2706.626824 0.040 Aug-‐13 2786.514269 3880.555683 0.393 Sep-‐13 2790.561057 3845.351377 0.378 Oct-‐13 2031.388907 3912.483847 0.926 Nov-‐13 1758.230914 3270.305518 0.860 Dec-‐13 2395.02846 2730.511095 0.140
2011-‐2013 NPV: 109,035 117,259 0.075 Total NPV: 6,440,001 6,448,225
PRESENT VALUE
PREDICTION PRESENT VALUE
R Square = 0.5694 R = 0.7546
Results
Software Matlab Neuroxl Predictor
Activation Function Hyperbolic Tangent Sigmoid Function
Hyperbolic Function
Number of Neurons 6 12 18 100 neurons
Predicted NPV Error 0.039 0.034 0.012 0.075
Prediction R square 0.9483 0.9626 0.9823 0.5694
Prediction R 0.9738 0.9811 0.9911 0.7546
NPV(2011-2013) 100,007 114,483 102,181 117,259
NPV 6,394,828 6,403,693 5,562,674 6,448,225
Recommendations
○ Use Reservoir Data as input attributes
○ Apply decline curve analysis DCA
○ Compare with Multiple Wells
○ Comparison to Real NPV Data
○ Use in house ANN for predicting
Conclusion
Utilizing a Neural Network to predict the Net Present
Value for a well is a viable option compared to
traditional Methods. Overall results proved that an ANN
can predict within an 8% error.
○ Time Efficient
○ May use simulation Data
○ Dependent on Technological Resources
○ May provide a forecast for a business investment
Thank you