شبکه های عملکردی به عنوان یک الگوی پیشگویانه داده کاوی جدید برای پیش بینی نفوذپذیری در مخازن کربناته

Permeability prediction has been a challenge to reservoir engineers due to the lack of tools that measure it directly. The most reliable data of permeability obtained from laboratory measurements on cores do not provide a continuous profile along the depth of the formation. Recently, researchers utilized statistical regression, neural networks, and fuzzy logic to estimate both permeability and porosity from well logs. Unfortunately, due to both uncertainty and imprecision, the developed predictive modelings are less accurate compared to laboratory experimental core data. This paper presents functional networks as a novel approach to forecast permeability using well logs in a carbonate reservoir. The new intelligence paradigm helps to overcome the most common limitations of the existing modeling techniques in statistics, data mining, machine learning, and artificial intelligence communities. To demonstrate the usefulness of the functional networks modeling strategy, we briefly describe its learning algorithm through simple distinct examples. Comparative studies were carried out using real-life industry wireline logs to compare the performance of the new framework with the most popular modeling schemes, such as linear/nonlinear regression, neural networks, and fuzzy logic inference systems. The results show that the performance of functional networks (separable and generalized associativity) architecture with polynomial basis is accurate, reliable, and outperforms most of the existing predictive data mining modeling approaches. Future work can be achieved using different structure of functional networks with different basis, interaction terms, ensemble and hybrid strategies, different clustering, and outlier identification techniques within different oil and gas challenge problems, namely, 3D passive seismic, identification of lithofacies types, history matching, rock mechanics, viscosity, risk assessment, and reservoir characterization

The process of oil exploration has been drastically improved by the availability of information technology, especially artificial intelligence with data mining modeling, as well as advances in seismic technology. Advances have pushed the envelope of what is feasible, both in terms of finding the oil and figuring out how to extract it once an oil and gas company has identified a location. Cutting edge advances in information technology and computational intelligence represent a breakthrough in oil and gas exploration and production. They have helped transform the business of exploration and production, increasing its production efficiency; and generating significant environmental benefits. Permeability is defined as the ability of porous rock to transmit fluid. It is one of the most crucial reservoir properties and is very difficult to calculate accurately. Generally, permeability is directly measured in a laboratory on rock samples. Last two decades, numerous efforts have been made to forecast permeability using well log data and available core data. The field of permeability prediction is vast, and incorporates many years of effort by expert petrophysicists, geologists and reservoir engineers. The problem with computational methodologies for permeability prediction from logs is the inherent inability of this method to integrate the geological and petrophysical controls on single phase flow. Carbonate reservoirs in particular are characterized by a very wide range of measured permeability for a given porosity, which reflects heterogeneity in pore size, geometry and connectivity. The relationships for forecasting permeability using wireline logs and core permeability data are based on statistical regression, feed-forward neural networks (FFNs), and fuzzy logic (FL) or adaptive neuro-fuzzy inference systems (ANFISs). Based on these approaches, better estimates have been reported compared to that of conventional techniques. Nevertheless, the application of neural networks to reservoir characteristics prediction is quite limited. Attempts were made to apply neural networks and fuzzy logic to permeability prediction, reservoir characteristics, pressure–volume–temperature prediction, and flow regimes and liquid-hold-up (Lucia et al., 2001, Bhatt and Helle, 2002, Nikravesh and Aminzadeh, 2000, Abdulraheem et al., 2007, Nikravesh and Aminzadeh, 2003, El-Sebakhy et al., 2007, Fangming et al., 2009 and El-Sebakhy, 2009). The findings of these works clearly indicate the potential of neural networks, which generally produces better results than those empirical formulae, derived from conventional multiple regression analysis. The results obtained from neural networks models are reasonably accurate. However, there is a need for further improvement when this method is applied to a complex structure. The use of combination of neural networks and fuzzy logic has also been reported in literature (Abdulraheem et al., 2007, Balan et al., 1995, Bhatt and Helle, 2002, Chen and Lin, 2006, Cross et al., 2010, Cuddy, 1997, El-Sebakhy, 2009, El-Sebakhy et al., 2007, Fangming et al., 2009, Jeirani and Mohebbi, 2006, Kadkhodaie-Ilkhchi et al., 2006, Kamali and Mirshady, 2004, Lim, 2005, Lucia, 2008, Lucia et al., 2001, Nikravesh and Aminzadeh, 2000, Nikravesh and Aminzadeh, 2003, Sears and Lucia, 2006 and Tamhane et al., 2000). Unfortunately, both neural networks and fuzzy logic inference systems are heuristic modeling approaches and suffer from a number of drawbacks, such as overfitting and local optima. In some cases, these techniques do not perform well, because the parameters in a training algorithm are based on initial guess of random weights, learning rate, and momentum. Therefore, there is a need for predictive modeling framework to estimate permeability and lithofacies from well logs in a carbonate reservoir. Recently, functional network has been proposed as a new intelligence data mining predictive model in solving numerous of prediction/classification problems, namely, pattern recognition, bioinformatics, engineering, software engineering, and business applications ( Bruen and Yang, 2005, Castillo et al., 1999, Castillo et al., 2001, El-Sebakhy, 2004, El-Sebakhy, 2009, El-Sebakhy, 2010, El-Sebakhy et al., 2007 and El-Sebakhy et al., 2010). It has been only utilized in solving two oil and gas industry problems, namely, predicting reservoir fluids PVT properties, Al-Bokhitan (2007) and rock mechanical parameters for hydrocarbon reservoirs, El-Sebakhy et al. (2010). The main motivation of this research is to investigate the strengths and capabilities of functional networks in forecasting the permeability from well logs in a hydrocarbon reservoir and compares its performance with the one of the most common statistics and artificial intelligence modeling techniques. The workflow of this research is designed as follows: Section 2 of this paper provides a brief literature review. Functional networks intelligent data mining system methodology and examples are described in detail in Section 3. The most common statistical quality measures in predictive modeling are proposed in Section 4. The use of functional networks in identifying the permeability from well logs implementations process and comparative studies are carried out in Section 5. Section 6 contains discussions of results, while Section 7 presents conclusions and recommendations

We have utilized numerous real-life databases of well logs to investigate the capabilities of functional networks intelligence predictive models to forecast permeability from well logs. Results have shown that the performance of the new functional networks intelligence paradigm with polynomial basis outperforms the most common existing statistical and data mining approaches with stable and reliable output permeability, especially in uncertain regions. Based on the above study, the following conclusions can be drawn. • The proposed computational intelligence scheme helps to overcome the limitations of the common soft-computing techniques, such as neural networks and statistical regression. • The developed functional networks predictive model has been applied with real-time new unseen wells. The obtained results show that the performance of this model is reliable and proper for real-time and for regular use. Furthermore, because of the strength of the functional networks intelligence system, it is expected that the proposed approach will perform equal to, if not better than, the other analytical and/or statistical methods. • The performance of functional networks with only second order degree basis polynomial families provides a reliable indicator to use it in different oil and gas industry applications such as multiphase flow regimes, history matching problems and risk analysis. The work can be extended to include more complex non-linear basis and ensemble learning mechanism, which may lead to better accuracy and more reliable real-time results in new reservoirs.