Application of Neural Network to the Determination of Well-Test Interpretation Model for Horizontal Wells

Abstract

TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractWell-test model identification and, subsequently, model parameters determination are more complex in horizontal wells than in vertical wells. This is due to the increase in number of flow regimes occurring during a flow period and the fact that strong correlation exists between model parameters.This study presents a new approach for automatic model identification and computer-aided well-test interpretation in horizontal wells. The new approach is based on using neural network to (1) identify the well-test interpretation model; (2) identify flow regimes; and (3) mark the position of identified flow regions on the derivative plot of well test data.This work consists of first generating common model signatures using Ozkan and Ragavan analytical solutions for horizontal wells in various reservoir and inner boundary conditions assuming laterally boundless reservoirs. Next, these signatures are used to train neural networks for three identification stages; model identification, flow regime identification, and position of flow regime identification. Separate networks were trained, then tested and validated using synthetic as well as field data. Once the three identification stages are completed, specialized plots for data points falling into each flow regime are used to determine initial model parameters. Final model parameters are determined using nonlinear regression.A comparative study was carried out using different network architectures. Modular approach with direct data utilization is found to be most suitable for implementation of our approach.

Assisted history matching using pattern recognition technology

“…Since that time, the applications of ANNs in addressing conventional problems of the petroleum industry have been widely studied. Some examples found in literature are: well log interpretation (Baldwin, Otte andWhealtley 1989, Masoud 1998, Jong-Se and Jungwhan 2004), well test data analysis (Al-Kaabi and Lee 1990, Ershaghi, et al 1993, Athichanagorn and Horne 1995, Sultanp and Al-Kaabi 2002, reservoir characterization (Mohaghegh, et al 1995, Ahmed, et al 1997, Singh, et al 2008, calibration of seismic attributes (David 1993), seismic pattern recognition (Yang and Huang 1991), inversion of seismic waveforms (Roth and Tarantoia 1992), prediction of PVT data (Briones, et al 1994, Gharbi and Elsharkawy 1997, Osman, Abdel-Wahhab and Al-Marhoun 2001, Oloso, et al 2009, fractures and faults identification (L. Thomas and Pointe 1995, Key, et al 1997, Sadiq and I.S. Nashawi 2000, Aminzadeh and deGroot 2005, hydrocarbons detection (Cheng-Dang, et al 1994, Aminzadeh anddeGroot 2005), formation damage forecast (Nikravesh, et al 1996, Kalam, Al-Alawi andAl-Mukheini 1996), and more.…”

The Application of Artificial Neural Networks With Small Data Sets: An Example for Analysis of Fracture Spacing in the Lisburne Formation, Northeastern Alaska

“…In this approach, the objective function is modified by adding a penalty term to prevent the large weights. In other words, the objective function, (4) where S e 2 is the mean squared error (MSE), N is the number of training samples, n is the number of weights, e i is the error of prediction of each training sample and λ is the performance ratio 14 , which forces the network to less overfit the data. λ = 0.5 is a common choice.…”

“…ANNs have numerous applications in geosciences and petroleum engineering e.g. permeability prediction 3 , fluid properties prediction 4 and well test data analysis 5 .…”

Artificial Intelligence Assisted History Matching -- Proof of Concept

“…• Application of NNs in well log interpretation (Baldwin, Otte, & Whealtley, 1989) (Jong-Se & Jungwhan, 2004) (Masoud, 1998) • Using NNs in well test data analysis (Al-Kaabi & Lee, 1990) (Ershaghi, Li, Hassibi, & Shikari, 1993) (Athichanagorn & Horne, 1995) (Sultanp & Al-Kaabi, 2002) • NNs a helpful tool in reservoir characterization (Mohaghegh., Arefi, Ameri., & Rose., 1995) (Ahmed, Link, Porter, Wideman, Himmer, & Braun, 1997) (Singh, Painuly, Srivastava, Tiwary, & Chandra, 2008) • Application of NNs to calibrate seismic attributes (David, 1993), seismic pattern recognition (Yang & Huang, 1991), inversion of seismic waveforms (Roth & Tarantoia, 1992) • Prediction of PVT data (Briones, Rojas, Moreno, & Martinez, 1994) (Gharbi & Elsharkawy, 1997) (Osman, Abdel-Wahhab, & Al-Marhoun, 2001) (Oloso, Khoukhi, Abdulraheem, & Elshafei, 2009) • Identifying fractures and faults (L. Thomas & Pointe, 1995) (Key, Nielsen, Signer, Sønneland, Waagbø, & H. Veire, 1997) (Sadiq & I.S. Nashawi, 2000) (Aminzadeh & deGroot, 2005) • Detecting hydrocarbons (Cheng-Dang, Wu, Mo, Zhu, & Xu, 1994) (Aminzadeh & deGroot, 2005), forecast formation damage (Nikravesh, Kovscek, Johnston, & Patzek, 1996) (Kalam, Al-Alawi, & Al-Mukheini, 1996) etc…”