A New Pore Pressure Prediction Method Back Propagation Artificial Neural Network

Transcription

1 A New Pore Pressure Prediction Method Back Propagation Artificial Neural Network Lianbo Hu a, *, Jingen Deng a, Haiyan Zhu b, Hai Lin a, Ziian Chen a, Fucheng Deng a, Chuanliang Yan a a State Key Laboratory of Petroleum Resource & Prospecting, China University of Petroleum (Beiing), Beiing, ,China b School of Petroleum Engineering, Southwest Petroleum University, Chengdu, Sichuan, , China * hlb1988cupb@163.com ABSTRACT Accurate pore pressure prediction is a necessary requirement to well structure optimizing, drilling difficulty minimizing, drilling accidents preventing. It plays a very important role in the economically and efficiently well operating. Previous methods for pore prediction have their own hypothesizes and cannot take into consideration factors that indicate or influence the pore pressure, so their applicability is limited to one or several regions and overpressure causes. This paper presents a new kind of pore pressure prediction BP neural network making use of the powerful learning capability of the neural network. Our model has three layers: two hidden layers and the output layer. The inputs of the first hidden layer include gamma ray and formation density. The result of the first hidden layer is 4 * input to the second hidden layer which additionally has depth, interval transit time and formation density as inputs. The optimized numbers of the neurons of each layer are 2, 5, and 1 respectively. The activation function of the first layer is hard-limit function, and the second and the third layer both has hyperbolic function as activation function. The feasibility and accuracy of our network is verified by the successful applications in a normal pressure field and an overpressure field. The average error is 4.61%. Compared with normal-structure pore pressure prediction neural network, our model doubled the accuracy of the prediction result. KEYWORDS: pore pressure prediction; artificial neural network; overpressure field; high accuracy

2 Vol. 18 [2013], Bund. S 4094 INTRODUCTION Pore pressure, also known as formation pressure, is the pressure which the fluid in the pore is subected to. Pore pressure is one of the most important parameters for well structure optimizing, economically and efficiently well operating, wellbore stability analyzing, etc. wellbore stability problems, kicks, or other serious problems such as blow-outs can be avoided with an accurate estimate of pore pressure, which means a good pore pressure prediction can not only save time and money but also guarantee the safety of drilling process. Having releasing the significant importance of pore pressure, researchers and engineers have done extensive work on the prediction of pore pressure since the classic paper written by Hottman and Johnson [1]. Hottman & Johnson s method for pore pressure prediction uses a crossplot to relate departures of a pore pressure indicator from the normal trend line to the pore pressure gradient at that depth. Using this method needs the establishment of the crossplot which means a lot of statistical work. Moreover, the crossplot developed at one region maybe improper for another region. Hottman & Johnson s method products accurate result only for prediction of pore pressure generated by under-compaction mechanism and may overestimate pore pressure in same region such as the deepwater Gulf of Mexico [2]. The Equivalent Depth method [3, 4] is another frequently mentioned pore pressure estimation techniques in the literature. If normal-pressured and over-pressured formations follow the same, unique relation for compaction as a function of effective stress, the Equivalent Depth method can provide a good estimation of pore pressure. Equivalent Depth method is valid only over limited depth ranges because of the fact that fluid properties and lithology change with depth [5] and it may underestimate the pore pressure of unloading formation [6]. Eaton [7, 8] published a new method in 1972 and enriched it in Eaton s method predicts pore pressure with parameters: interval transit time, resistivity, conductivity, and dxc-exponent. However, Eaton s method uses these parameters individually without taking into consideration the combined influence of these parameters. Moreover, Eaton s method is inapplicable for pore pressure prediction of unloading formation as it underestimates the overpressure generated by fluid expansion [6]. Bowers developed a novel method in 1995 and complemented is in This method provides good estimation for unloading formation, but it requires a proper understanding of the stress state of the formation. These methods are used extensively and efficaciously, and more methods were listed in literature [2]. Summarizing these methods, we will acknowledge that they ust utilize part of the factors which indicate or influence the pore pressure without an overall consideration of these factors and that their applicability is limited to one or several regions and overpressure causes. Artificial Neural Networks (ANNs) are powerful and efficient tools for dealing with complicated problems and can make overall generalization. It is very tempting to use ANNs for predicting pore pressure. Artificial Neural Network (ANN) [10] is a method which can simulate the capability of receiving signals and making proper responses of biological neural network. It was introduced [6, 9]

3 Vol. 18 [2013], Bund. S 4095 half a century ago, and is utilized in numerous fields including petroleum industry. T. Sadiq and I. S. Nashawi [11] established a Back Propagation Neural Network (BPNN) and Generalized Regression Neural Networks (GRNN) for predicting the formation fracture gradient and compared their performance. It is revealed in the study that the GRNN provides an adequate approximation of fracture gradient as a function of depth, overburden stress gradient and Poisson s ratio. Manabu Doi [12] successfully developed a BPNN to predict the uniaxial tensile strength formation. C. Siruvuri [13] et al established a neural network for understanding the causes of stuck pipe and verified its feasibility with actual data from Gulf of Mexico. Rahman Ashena [14] developed an Artificial Neural Network for evaluating bottom hole pressure in the inclined annulus and applied it successfully in two maor Iranian Oil Fields. According to his literature, his ANN model shows a much better performance than Naseri et al mechanistic model. Keshavarzi, R. et al. [15] developed a feed-forward with back-propagation neural network (FFBPNN) to predict the fracture gradient as a function of pore pressure gradient, depth and rock density in one of southern oilfields in Iran, and the results indicate that the neural network approach is not only feasible but also yield quite accurate outcome. Jahanbakhshi, R. and Keshavarzi, R. [16] successfully applied the ANN approach to prediction the wellbore stability risk. The ANN was developed in such a way that in-situ stresses, drilling string properties, drilling operation, geological conditions and mud properties were the inputs and wellbore stability/instability was the output. ANN was also used for pore pressure estimation before. Liang changbao et al. [17] use a common three-layer BPANN for predicting of pore pressure and fracture pressure. The inputs of the BPANN include interval transit time, formation density, and the depth. However, the literature did not provide any application case of the established model. Husam AlMustafa [18] combined an unsupervised vector quantization (UVQ) analysis and a supervised vector quantization (SVQ) to identify the acoustic impedance along with its Fourier transform as potential attributes indicative of geopressure and select attributes to generate a map consisting of 2 distinct target classes, one reflecting a zone of geopressure, and the other a normal formation-pressure zone. Successful application verified the model, but its process is complex. James W. Bridges [19] mentioned using ANN to predict pore pressure. However, as pointed out in the paper, the results were inconclusive. Xue Yadong, Gao Deli [20] applied ANN to the recognize the deep formation pore pressure. In this paper, the formation depth is compared to a time axis, and then the pore pressure can be assimilated to time series. Last, the ANN was use for recognizing the pore pressure. The ANN used in this paper also falls into the common and wildlyused category. What should be pointed out is that they utilized the normal pore pressure data of a well to train the ANN and then applied the trained ANN to recognize the normal pore pressure of the lower formation in the same well and that they utilized the overpressure data of the well to train the ANN and then applied it to recognize the overpressure of the lower formation in the same well. The successful case verified the feasibility of the approach, however, according to the literature, the number of iterations that the training process needed to converge reached up to David N. Dewhurst [21] utilized ANN to calculate the pore pressure with seismic data.

4 Vol. 18 [2013], Bund. S 4096 However the ANN was used for prediction the net stress. Hu Quanming and Ji Haipeng [22] applied ANN to predict the pore pressure the Saertu oil field and Xingshugang oil field in Daqing. According to the literature, the inputs of their ANN model included log data such as interval transit time, spontaneous potential, gamma ray, and 3-resistivity record and the average error of the ANN is approximate 8.9%. Without depth as one of the inputs, the provided result is questionable. The structure or the topology of the ANNs mentioned above is common and wildlyused. The goal of this paper is to present a new pore pressure prediction ANN model, to verify its feasibility and accuracy and to discuss the influence of the parameters of the ANN. MODEL ESTABLISHMENT Artificial neuron model Artificial neural network [10] can loosely simulate the structure and capability of biological neural network: network node or artificial neuron works as biological neuron and connection weights of the neural network function as chemical transmitter and electric transmitter. An ANN has the capability of intelligent analyzing with simple mathematical method and dealing with non-linear, fuzzy, and complex relationship. Artificial neuron is the basic element of an ANN, whose model is showed in Figure 1. The function of artificial neuron, as the name implies, is to simulate the biological neuron. x 1 1 Sum Activation Function x 2 2 S f ( S ) y x p p Threshold Output Weights Figure 1: The basic artificial neuron model The artificial neuron showed in Figure 1 has P inputs and one output. The inputs are x i ( i=1,2,,p) and the output is y. The relationship of inputs and output can be formulated as following:

5 Vol. 18 [2013], Bund. S 4097 s y p wi x i1 i f ( s ) (1) Where, is the threshold; wi is the connection weight or weight from signal i to the neuron ; s is the net activation; f ( ) is called activation function. There are a lot of activation function including: linear function, ramp function, threshold function, squashing function etc. FFBP artificial neural network The neurons are arranged and organized in different forms depending on the type of the network. These neurons are organized in the form of layers. Then layers are linked by the connection weight, and an ANN is formed. After half of a century s research, researches established numerous types of ANN. Feed-forward with back-propagation artificial neural network (FFBPANN) is well-known and widely used in engineering applications. The structure or topology of a typical feed forward neural network is showed is Figure 2. Model inputs Model outputs Output layers Input nodes Hidden layers Figure 2: The structure or topology of a typical feed forward neural network The neural network showed in Figure 2 has three layers (the inputs nodes are not counted as a layer of the neural network): two hidden layers and the output layer. Each neuron in a certain layer receives the signals coming from the upper layer and produces an output using the algorithm described in formula (1). The outputs of all the neurons of a certain layer are the output of that layer. Before an ANN can be used to perform its task, it should be trained to do so, that s to say, it need a training or learning process. The training process is a procedure to determine the weights and thresholds using an appropriate learning algorithm. FFBPANN which this paper also uses applies the error back propagation approach to realize the goal. The training process needs a

6 Vol. 18 [2013], Bund. S 4098 sample database. Generally, the data in the database is randomly divided into 3 subsets: the training set, the validation set, and the test set. The training set is used for calculating weights and thresholds. The validation set is used for stopping training process to prevent the network from overfitting the data. The error which is defined as the difference between the predicted output and the desired output (target) on the validation set is monitored during the training process. The validation error will normally decreases in the initial stage of the training process, so does the training set error. However, when the network begins to overfit the data, the validation error begins to rise. When the validation error increases for a specified number of iterations, the training is completed and the weights and thresholds are determined. The test error is used for compare different models. As mentioned in the introduction part of this paper, ANNs are used for predicting the bottom hole pressure, formation fracture pressure, pore pressure, and the wellbore stability. According to the literatures, the structure or the topology of these ANNs belongs to the common and wildlyused type. However, an ANN with this type of structure cannot perfectly simulate the process we calculate the pore pressure with log data. This paper presents a new kind of pore pressure prediction ANN with a novel structure. New kind of pore pressure prediction FFBPANN It takes several steps to establish an ANN model. Firstly, we should determine the type of ANN and the output parameters. After decades of research, researches established numerous types of ANN. However, only several kinds of them are wildly used. Some kinds of them such as sensation network and linear network are only capable for problem with linear separability or linear relationship. FFBPANN is the most wildly used ANN due to its powerful adaptability for different problem and excellent performance when dealing with complex relationship. This paper also applies FFBPANN to pore pressure prediction. The output of our ANN is pore pressure. Then the next important ob is to determine the number of the network layers, the link condition of the layers, the number of neurons on each layers and the activation function. Previous research reveals that a three-layer BPANN can almost approximate any nonlinear function, so for a comparatively simple problem such as the pore pressure prediction in this paper, a three-layer BPANN is competent [23]. If an ANN has too many layers than the problem really need, the ANN will overfit the training data. The determination of the linking condition of the layers depends on the problem to solve and the principle is the ANN with optimized linking relationship can better simulate the process we human beings solve the same problem. The number of neurons on each layer is greatly influenced by the complexity of the problem and the structure of the ANN. An ANN with underestimated neurons cannot solve the problem and its capacity is limited while an ANN with too many neurons also can overfit the training data. So the number of neurons on each layer is determined by some empirical methods or try-and-error method. The selection of activation function depends on the rough relationship between the inputs and the outputs. A higher degree of linear relationship between the inputs and the outputs requires the ANN has

7 Vol. 18 [2013], Bund. S 4099 more linear activation functions, or the ANN needs more nonlinear activation functions. The inputs parameters, in some situation, are definite, but in some cases such as the pore pressure in this paper, inputs parameters are optional. It is well known that we can use different logging data to predict the pore pressure. In this paper the performance of ANNs with different combination of logging data will be compared and the optimal combination will be recommended. What should be pointed out is that all the data in the database and the data used for predicting pore pressure should be normalized, and the output of the ANN should be reversely normalized to get the real pore pressure. After optimizing, this paper established a new pore pressure prediction FFBPANN, whose structure is showed as Figure 3. The first set of inputs The second set of inputs Input nodes Hidden layers Output layer Pore pressure Figure 3: The structure of our FFBPANN for pore pressure prediction Our optimized FFBPANN has three layers: two hidden layers and the output layer. The first set of inputs which includes gamma ray, formation density is the inputs of the first layer. The second set of inputs, which includes interval transit time, formation density and depth, and the result of the first layer are the inputs of the second layer. Compared with the ANNs used in the literatures mentioned before, the novel design of this ANN is the fact that it has two sets of inputs and a different structure. It is this novel design that enables the ANN to generate a more accurate pore pressure prediction with logging data because it can simulate the process we predict the pore pressure more precisely. In the discussion section, the author will explain why this structure and combination of logging data is chosen.

8 Vol. 18 [2013], Bund. S 4100 FIELD APPLICATION To verify the feasibility, the reliability and the accuracy of our ANN model, five wells selected from two different oil fields are used for training and verifying the ANN. Two of these five wells including well A and well B come from LiuHua oil field located in the Pearl River Mouth Basin of South China Sea and have normal pore pressure distribution. The rest of these wells including well C, well D and well E come from WeiZhou oil field located in the Beibuwan Basin of South China Sea and have abnormal pressure. The logging data and pore pressure data (measured or/and predicted) of these five wells is presented from Figure 4 to Figure 8. the DT stands for the interval transit time, Gamma ray is the gamma logging of the formation, the Density represents the formation density, and the Pressure is the equivalent mud density of the formation pore pressure (measured or/and predicted). DT(us/ft) Gamma ray Density(g/cm^3) Pressure(g/cm^3) Figure 4: The profile map of logging data and pore pressure (known) of well A

9 Vol. 18 [2013], Bund. S 4101 DT(us/ft) Gamma ray Density (g/cm^3) Pressure(g/cm^3) Measured data 深度 (m) Our model Figure 5: The profile map of logging data and pore pressure of well B DT(us/ft) Gamma ray Density(g/cm^3) pressure(g/cm^3) Figure 6: The profile map of logging data and pore pressure (known) of well C

10 Vol. 18 [2013], Bund. S DT(us/ft) Gamma ray Density(g/cm^3) Pressure(g/cm^3) Measured data Eaton's method Our method Figure 7: The profile map of logging data and pore pressure of well D DT(us/ft) Gamma ray Density(g/cm^3) Pressue(g/cm^3) Eaton's method th d Depth Our method Measured data Figure 8: The profile map of logging data and pore pressure (known) of well E 1) The logging data and pore pressure of well A acts as training database to training our ANN and then the trained ANN is applied to predict the pore pressure of well B with its logging data showed in Figure 5. Both the result and measured data are showed in Figure 5. From the graph, it is obvious that the predicting result is excellent and the trained ANN does well. Because pore pressure prediction dose not focus on the normal pressure formation, the following discussion

11 Vol. 18 [2013], Bund. S 4103 will not contain the normal pressure prediction. This example is used for verifying the capability and feasibility of our model for normal pore pressure prediction. 2) The logging data and pore pressure of well C acts as training database to training our ANN and then the trained ANN is applied to predict the pore pressure of well D and well E with their logging data showed in Figure 7 and Figure 8 respectively. The result and measured data of well D and well E are also showed in Figure 7 and Figure 8 respectively. The result showed in Figure 7 and Figure 8 verifies the feasibility and the capability of our model. In the following section, the influence of the ANN parameters on the prediction result will be discussed with the data of wells coming from WeiZhou oil field. DISCUSSION The influence of the structure of the ANN As mentioned before, ANNs used for predicting the bottom hole pressure, formation fracture pressure, pore pressure, and the wellbore stability have the structure or the topology showed in Figure 2. However, the author found that the ANNs with the structure cannot perform well in pore pressure prediction. To solve the problem, we should analyze the process we calculate the pore pressure using conventional methods. Because most of these methods are based on the theory of shale compaction, before we use the logging data to predict calculate the pore pressure we need to exclude the logging data of non-shale formation with the criterions such as the gamma ray [24]. It is obvious that a reasonable and competent pore pressure prediction ANN should have this ability, and that the ANN with structure showed in Figure 2 is lack of this ability. Considering the shortage of common pore pressure prediction ANN, the author develops a newstructured pore pressure prediction ANN. Our model can partly realize the process of excluding the logging data of non-shale formation since the result of the first layer whose inputs are the indicators of formation lithology can reveals the influence of formation lithology. The result of common ANN and our model is presented and compared. Moreover, the result of Eaton s method is also presented. The comparative criterions include the maximum, minimum and average value of the prediction error. The subsequent comparative analysis also applies these criterions. The comparative result is listed in Table.1. The data of the Table.1 reveals that the performance our model is much better that the conventional ANN and the accuracy is doubled. Table 1: The error of different methods maximum error minimum error average error Method well D well E well D well E well D well E average conventional 26.08% 35.41% 0.04% 0.13% 5.95% 14.13% 10.4% ANN our ANN 10.76% 14.81% 0.00% 0.50% 2.07% 7.15% 4.61% Eaton s 31.71% 33.09% 0.07% 0.39% 6.55% 8.05% 7.03%

12 Vol. 18 [2013], Bund. S 4104 The prediction result of well D and well E using Eaton s method is showed in Figure 7 and Figure 8 respectively. From Figure 7 and Figure 8, it is revealed that the result of our model has the same trend with the Eaton s method, but the result of Eaton s method changes roughly and has a lower smoothness than that of our model. The error of Eaton s method also listed in Table.1, from which we find that the maximum error of Eaton s method is much bigger than our model while the difference of the average error of these two methods is small. Eaton s method is more sensitive to the formation lithology change than our model and the comparison also verifies the feasibility and accuracy of our model. The influence of the neurons number There are numerous methods to determine the number of neurons on each layer, but most of them are empirical or semi-empirical approaches [23]. Try-and-error method is also used for determining the number of neurons. This paper compares the results of several our ANN models with different number of neurons and the errors of these ANNs are listed in Table.2. Table.2 reveals that the number of neurons has a great influence on the prediction result and that the best combination of the neurons is which means the first layer has two neurons and the second layer has five neurons and the output layer has one neuron. first layer Table 2: the errors of ANNs with different number of neurons number of neurons maximum error minimum error average error second layer output layer well D well E well D well E well D well E average % 17.30% 0.08% 0.01% 4.00% 9.70% 6.85% % 15.42% 0.02% 0.02% 3.39% 7.30% 5.35% % 14.81% 0.00% 0.50% 2.07% 7.15% 4.61% % 16.41% 0.00% 0.00% 3.80% 8.93% 6.37% % 17.29% 0.59% 0.01% 4.37% 9.71% 7.04% % 17.53% 0.04% 0.00% 5.71% 7.97% 6.84% The influence of the activation function As introduced before, the selection of activation function depends on the rough relationship between the inputs and the outputs and it is difficult to determine the activation functions [23] and try-and-error method is also effective one to do the ob. It has mentioned that activation function includes linear function, ramp function, threshold function, squashing function et al. This paper compares the results of several our ANN models with different activation function combination and the errors of these ANNs are listed in Table.3. In Table.3, h stands for hardlim function which belongs to threshold function, t represents hyperbolic function which belongs to squashing function and s is short of saturating linear function which falls into ramp function. Table.3 reveals

13 Vol. 18 [2013], Bund. S 4105 that the selection of activation function has a great influence on the prediction result and that the best combination of activation functions is h-t-t. The error of the ANN s output increases with the increasing of its linear activation function. The comparison result is not a surprise, considering the fact that relationship of the logging data and the formation pore pressure highly non-linear. The activation functions of our optimized ANN for each layer are h-t-t which means activation function of the first layer is hardlim function and the activation of the second layer and the output layer is the hyperbolic function. first layer Table 3: the errors of ANNs with different activation functions activation function maximum error minimum error average error second layer output layer well D well E well D well E well D well E average h t t 10.76% 14.81% 0.00% 0.50% 2.07% 7.15% 4.61% s t t 8.73% 16.75% 0.39% 0.19% 4.83% 8.66% 6.75% h t s 38.07% 34.47% 13.51% 7.47% 24.36% 16.67% 20.52% h s t 11.55% 15.18% 0.47% 0.80% 5.20% 8.90% 7.05% h s s 38.07% 34.47% 13.51% 7.47% 24.36% 16.67% 20.52% The influence of the inputs parameters The formation parameters that conventional methods use for pore pressure prediction are part of logging data including interval transit time, resistivity, conductivity, dxc-exponent and gamma ray el at [1-9]. Because of the character of the ANN that it can have several inputs and does not need a clear formulated relationship between these inputs and the outputs. We can take into consideration as more factors that influence or indicate the overpressure as possible. This paper compares the results of several our ANN models with different combinations of the logging data and the errors of these ANNs are listed in Table.4. Table.4 reveals that the error of ANN with inputs of the third combination is the smallest. The first set of inputs of the third combination includes interval transit time, formation density, and the depth. The second set of the inputs includes gamma ray and the formation density. Table 4: The errors of ANNs with different inputs combination inputs combination maximum error minimum error average error The first set of The second inputs set of inputs well D well E well D well E well D well E average interval transit time, density gamma ray 51.89% 34.54% 0.02% 0.09% 15.39% 19.41% 17.40% interval transit time, density, depth gamma ray 15.79% 15.42% 0.02% 0.02% 3.39% 7.30% 5.35% interval transit gamma ray, time, density, depth density 10.76% 14.81% 0.00% 0.50% 2.07% 7.15% 4.61%

14 Vol. 18 [2013], Bund. S 4106 CONCLUSIONS The paper presents a new pore pressure prediction FFBPANN. The FFBPANN model has three layers: two hidden layers and one output layer. The inputs of the first layer include gamma ray and formation density and the inputs of the second layer include depth, interval transit time, formation density and the result of the first layer. The feasibility and accuracy of our model is verified by the successful application in five wells coming from two fields. The maximum average errors of our model is 7.15%, and the average value of average errors is 4.61%,The results indicate that our model is not only feasible but also yield quite accurate outcome. The numbers of the neurons of each layer are 2, 5, and 1 respectively. The activation function of the first layer is hard-limit function, and the second and the third layer both has hyperbolic function as activation function. With powerful learning ability and excellent adaptability, the trained ANN can predict accurate pore pressure which can save a lot of time and parameters our ANN use are vary available. If new data is available, our ANN model is able to be further trained. REFERENCES 1. Hottman,C. E. & Johnson,R. K (June1965). Estimation of Formation Pressures from Log-Derived Shale Properties. Journal of Petroleum Technology, Vol. 6, pp Glenn Bowers (1999). State of the Art in Pore Pressure Estimation. Applied Mechanics Technologies, report 3. Foster, J. B. & Whalen, J. E (1966). Estimation of formation pressures from electrical surveys-offshore Louisiana. Journal of Petroleum Technology, Vol. 2, pp Ham, H. H. (1966). A method of estimating formation pressures from Gulf Coast well logs. Trans.-Gulf Coast Assn. Of Geol. Soc, Vol. 16, pp Haixiong Tang, JunFeng Luo, Kaibin Qiu et al (2011). Worldwide Pore Pressure Prediction: Case Studies and Methods. In: SPE Asia Pacific Oil and Gas Conference and Exhibition, Jakarta, Indonesia 6. Glenn Bowers (1995). Pore Pressure Estimation from Velocity Data; Accounting for Overpressure Mechanisms Besides Undercompaction. SPE Drilling & Completions, Vol. 6, pp Ben A. Eaton (1972). The Effect of Overburden Stress on Geopressure Prediction from Well Logs. Journal of Petroleum Technology, Vol.8, pp Ben A. Eaton (1975). The Equation for Geopressure Prediction from Well Logs. SPE Glenn Bowers (2001). Determining an Appropriate Pore-Pressure Estimation Strategy. In: Offshore Technology Conference, Houston, Texas 10. Jiang Zongli (2001) Introduction to Artificial Neural Networks. Higher Education

15 Vol. 18 [2013], Bund. S 4107 Press, Beiing (in Chinese) 11. T. Sadiq, & I. S. Nashawi (2000). Using Neural Networks for Prediction of Formation Fracture Gradient. In: SPE/Petroleum Society of CIM International Conference on Horizontal Well Technology, Calgary, Alberta, Canada 12. Manabu Doi, Takahiro Murakami et al (2000). Field Application of Multi- Dimensional Diagnosis of Reservoir Rock Stability Against Sanding Problem. In: SPE Annual Technical Conference and Exhibition, Dallas, Texas 13. C. Siruvuri, & S. Nagarakanti, R. Samuel (2006). Stuck Pipe Prediction and Avoidance: A Convolutional Neural Network Approach. In: IADC/SPE Drilling Conference, Miami, Florida 14. Rahman Ashena, J.Moghadasi et al (2010). Neural Networks in BHCP Prediction Performed Much Better Than Mechanistic Models. In: CPS/SPE International Oil & Gas Conference and Exhibition, Beiing, China 15. Keshavarzi, R, Jahanbakhshi, & R., Rashidi, M (2011). Predicting Formation Fracture Gradient in Oil and Gas Wells: A Neural Network Approach. In: the 45th US Rock Mechanics / Geomechanics Symposium, San Francisco 16. Jahanbakhshi, & R, Keshavarzi, R. (2012). Intelligent Prediction of Wellbore Stability in Oil and Gas Wells. In: the 46th US Rock Mechanics / Geomechanics Symposium, Chicago 17. Liang Changbao et al (1996). Establishing Pore Pressure Prediciton Model by BP Neural Network. Computer Applications of Petroleum Vol. 3, No. 4, pp Husam AlMustafa, & Saeed AlZahrani, Saudi Aramco (2001) Geopressure Detection Using Neural Classification of Seismic Attributes. In: SEG Int'l Exposition and Annual Meeting, San Antonio, Texas 19. James W. Bridges (2003). Summary of Results from a Joint Industry Study to Develop an Improved Methodology for Prediction of Geopressures for Drilling in Deep Water. In: SPE/IADC Drilling Conference, Amsterdam, The Netherlands 20. Xue Yadong, & Gao Deli (2003), Intelligent Recognition Method of Deep Formation Pore Pressure. Chinese Journal of Rock Mechanics and Engineering. Vol. 22, No. 2, pp (in chinese) 21. David N. Dewhurst, Anthony F. Siggins (2004). A core to seismic method of pore pressure prediction. In: the 6th North America Rock Mechanics Symposium (NARMS): Rock Mechanics Across Borders and Disciplines, Houston, Texas 22. Hu Quanming, & Ji Haipeng (2010). Pressure Prediction Technology of the Deep Strata, Based on the BP Neural Network. Vol. 21, pp (in Chinese) 23. Wu Changyou (2007). The Research and Application on Neural Network. PhD. Thesis, Northeast Agrieultural University (in Chinese) 24. Peter van Ruth, & Richard Hillis (2000). Estimating pore pressure in the Cooper Basin, South Australia:sonic log method in an uplifted basin. Exploration Geophysics, Vol. 31, pp , EJGE