Application of Seismic Attribute Technique to estimate the 3D model of Hydraulic Flow Units A case study of a gas field in Iran Egyptian Journal of Petroleum xxx (2017) xxx–xxx Contents lists availabl[.]
Egyptian Journal of Petroleum xxx (2017) xxx–xxx Contents lists available at ScienceDirect Egyptian Journal of Petroleum journal homepage: www.sciencedirect.com Full Length Article Application of Seismic Attribute Technique to estimate the 3D model of Hydraulic Flow Units: A case study of a gas field in Iran Mohamad Iravani a, Mahdi Rastegarnia b, Dariush Javani c, Ali Sanati d,⇑, Seyed Hasan Hajiabadi d a Department of Petroleum Engineering, Islamic Azad University, Science and Research Branch, Tehran, Iran Department of Petrophysics, Pars Petro Zagros Engg & Services Company, Tehran, Iran c Department of Mining Engineering, Imam Khomeini International University, Ghazvin, Iran d Faculty of Petrochemical and Petroleum Engineering, Hakim Sabzevari University, Sabzevar, Iran b a r t i c l e i n f o Article history: Received 20 January 2017 Revised February 2017 Accepted 14 February 2017 Available online xxxx Keywords: Multi-attribute analysis Seismic attribute Flow Zone Index Acoustic impedance volume Hydraulic Flow Units a b s t r a c t One of the most important steps in evaluation and development of hydrocarbon reservoirs is the mapping of their characteristics Nowadays, Seismic Attribute Technique is used to build parameters of hydrocarbon reservoirs in inter-well spaces One of these parameters is the Flow Zone Index (FZI) that has a significant effect on different stages of evaluation, completion, primary and secondary production, reservoir modeling and reservoir management The aim of this study is to introduce an equation using seismic attribute and FZI log in wells and then generalize it to predict FZI throughout the reservoir For this purpose, acoustic impedance (AI) volume as an external attribute was created while internal attributes were computed from seismic data After that, The best set of attributes was determined using stepwise regression after which seismic attributes were applied to multi-attribute analysis to predict FZI Then, the attribute map resulted from multi-attribute analysis was used to interpret the spatial distribution of the gas bearing carbonate layers Finally, the optimum number of Hydraulic Flow Units (HFU) was determined by analyzing the break point in the plot of cumulative frequency of FZI for wells and was generalized all over the reservoir by using the 3D HFU model The results demonstrated that multi-attribute analysis was a striking technique for HFU estimation in hydrocarbon reservoirs that reduces cost and increases rate of success in hydrocarbon exploration Distribution of producible hydrocarbon zones along with the seismic lines around the reservoir was characterized by studying this model which can help us in choosing the location of new wells and more economical drilling operations Ó 2017 Egyptian Petroleum Research Institute Production and hosting by Elsevier B.V This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Introduction One of the most important steps in evaluation and development of hydrocarbon reservoirs is the mapping of their characteristics Numerous empirical relations between seismic attributes and well log data have been introduced for estimation of physical properties such as porosity, permeability, etc [1–7] Multi-attribute analysis is an effective method to use well logs in conjunction with seismic data for prediction of well log properties from the seismic responses In this paper multi-attribute method is used to examine the prediction of Flow Zone Index (FZI) logs from seismic attributes Permeability and FZI has a significant effect on different stages of evaluation, completion, primary and secondary produc- Peer review under responsibility of Egyptian Petroleum Research Institute ⇑ Corresponding author E-mail address: ali.sanati@yahoo.com (A Sanati) tion, reservoir modeling and reservoir management Therefore, different methods have been used to evaluate FZI by petroleum engineers [8–13] Rezaee et al introduced a new approach to determine hydraulic flow unit (HFU) by the current zone indicator (CZI) and electrical flow unit (EFU) concepts [14] Recently, Dezfoolian et al presented an intelligent model based on probabilistic neural networks (PNN) to produce a quantitative formulation between seismic attributes and HFU in Kangan and Dalan carbonate reservoirs [15] Rastegarnia and KadkhodaieIlkhchi used seismic attribute analysis to predict FZI using seismic and well log data They showed that it is an effective technique to predict FZI in an oil reservoir [16] Moreover, Yarmohammadi et al delineated high porosity and permeability zones by using the seismic derived flow zone indicator data at the Shah Deniz sandstone packages [17] Rastegarnia et al studied the application of seismic attribute analysis and neural Network to estimate the 3D FZI and electrofacies of a hydrocarbon reservoir They found that these http://dx.doi.org/10.1016/j.ejpe.2017.02.003 1110-0621/Ó 2017 Egyptian Petroleum Research Institute Production and hosting by Elsevier B.V This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Please cite this article in press as: M Iravani et al., Application of Seismic Attribute Technique to estimate the 3D model of Hydraulic Flow Units: A case study of a gas field in Iran, Egypt J Petrol (2017), http://dx.doi.org/10.1016/j.ejpe.2017.02.003 M Iravani et al / Egyptian Journal of Petroleum xxx (2017) xxx–xxx methods are successful in modeling of FZI and electrofacies from 3D seismic data [18] In this study, FZI was estimated by use of seismic attribute analysis in one of Iranian gas fields For this purpose, acoustic impedance volume as an external attribute was created and the internal attributes were computed from seismic data The best set of attributes was determined using stepwise regression Seismic attributes were applied to multi-attribute analysis to predict FZI The attribute map resulted from multi-attribute analysis was then used to interpret spatial distribution of the gas bearing carbonate layer Petrophysical data of two wells, 3D seismic data cube, and structurally interpreted data from a gas field in the southwest of Iran were used in this study Study area Fig Geographical location of the gas field A The gas field A, is one of the largest gas accumulations in the world containing about 8.5 trillions cubic meters of gas In the Persian Gulf zone, the Permian gas basin is known as the Khuff Formation This sequence is composed mainly of carbonate rocks and is extended in Bahrain, Qatar, Abu Dhabi, Saudi Arabia, and Iran (Fig 1) [19] Data in this study came from two wells namely SP5 and SP9 penetrated into the specific reservoir in the field for which FZI data and petrophysical logs were available Figs and show the wells used in this work to build a spatial distribution of FZI As can be seen, there is a good relationship between FZI with saturation gas and lithology The Dalan formation is divided into Fig Well SP9 used in this study to build a 3D distribution of FZI in K4 member of Dalan formation Please cite this article in press as: M Iravani et al., Application of Seismic Attribute Technique to estimate the 3D model of Hydraulic Flow Units: A case study of a gas field in Iran, Egypt J Petrol (2017), http://dx.doi.org/10.1016/j.ejpe.2017.02.003 M Iravani et al / Egyptian Journal of Petroleum xxx (2017) xxx–xxx Fig Well SP5 used in this study to build a 3D distribution of FZI in K4 member of Dalan formation Dashtak Kangan Seythian L.Sudair U Dalan LITHOLOGY PET PLAY Shale and Clay with Dolomitic Intercalations Seal Aghar Mbr Shale Kangan Mbr Nar Mbr Shale Dolomite+Anhydrite Anhydrite+Dolomite Dolomite Limestone Anhydrite+Dolomite Anhydrie Dolomite Limestone Anhydrite+Dolomite Anhydrite K5 Limestone+Dolomite K1 K2 K3 L Dalan UPPER MIDDLE L.TRIASSIC FORMATION PERMIAN PALEOZOIC MESOZOIC AGE K4 Gas Gas Fig Stratigraphic of Permian-Triassic sequence in the Persian Gulf field Please cite this article in press as: M Iravani et al., Application of Seismic Attribute Technique to estimate the 3D model of Hydraulic Flow Units: A case study of a gas field in Iran, Egypt J Petrol (2017), http://dx.doi.org/10.1016/j.ejpe.2017.02.003 M Iravani et al / Egyptian Journal of Petroleum xxx (2017) xxx–xxx Fig Characteristic of the extracted wavelet Fig Calculated correlation coefficient between real trace seismic and synthetic traces for well sp9 four zonations that are composed of biogenic limestone dolomite and thin bed of anhydrite (Fig 4) In this study, we used the 3D seismic data to make a 3D HFU model for K4 section of Dalan formation Methodology The aim of this study is to apply multi-attribute analysis of seismic data for prediction of FZI throughout the Dalan formation For this purpose, post stack 3D seismic and well log data of two wells were used (Figs and 2) Moreover, porosity logs and FZI log were available for all wells, but check shot data were only available in one well In order to build a 3D model of FZI the below procedure was followed based on Rastegarnia and Kadkhodaei-Ilkhichi [16]: First, an acoustic impedance model was created using modelbased inversion that used as an external attribute for the creation of a 3D FZI model; Then the equation for correlating seismic attributes to FZI log was determined for wells in the field This equation is applied for estimation of the FZI log in the intervals between the wells Finally, the optimal number of HFUs was determined using the plot of cumulative frequency of FZI for both wells and was generalized on the created 3D HFU model 3.1 Model-based inversion Seismic inversion is a preliminary study in reservoir characterization Therefore, there is a continuous effort to optimize the inversion algorithm and improve the resolution of the inverted volume The amplitude-based seismic data were processed through a Please cite this article in press as: M Iravani et al., Application of Seismic Attribute Technique to estimate the 3D model of Hydraulic Flow Units: A case study of a gas field in Iran, Egypt J Petrol (2017), http://dx.doi.org/10.1016/j.ejpe.2017.02.003 M Iravani et al / Egyptian Journal of Petroleum xxx (2017) xxx–xxx Fig Correlation coefficient resulted in initial model for well sp9 Fig Broadband acoustic impedance inversion based on model Please cite this article in press as: M Iravani et al., Application of Seismic Attribute Technique to estimate the 3D model of Hydraulic Flow Units: A case study of a gas field in Iran, Egypt J Petrol (2017), http://dx.doi.org/10.1016/j.ejpe.2017.02.003 M Iravani et al / Egyptian Journal of Petroleum xxx (2017) xxx–xxx Fig The multi-attribute analysis showing the average RMS and validation error; the optimum number of attributes is equal to Table Multi-attributes extracted for predicting the FZI Target Final attribute Training error (mm) Validation error (mm) FZI FZI FZI FZI FZI FZI FZI Acoustic impedance Amplitude envelope Filter 15/20–25/30 Filter 45/50–55/60 Filter 55/60–65/70 Quadrature trace Cosine instantaneous phase 55.15 48.70 45.17 42.38 38.6 35.31 29.94 74.90 71.03 68.171 84.803 96.03 69.22 49.84 model-based inversion algorithm to produce acoustic impedance volume which was used as an external attribute in the multi- attribute analysis [20] For this purpose, sonic log data were corrected via available seismic data (check shot), geological horizons were determined, picked and interpreted in seismic lines and then best synthetic wavelet and seismogram were extracted Fig shows the extracted identification wavelet Well logs and seismic were calibrated and the highest correlation coefficient between real traces seismic and synthetic traces seismic was obtained using calculated wavelet (Fig 6) Afterwards, an initial model has been developed and inverted for error correction (Fig 7) To perform the model-based inversion, a geological model was compared with seismic data In order to find a better match, the result of comparison is then used to iteratively update the model Since this model does not use the direct inversion of the seismic data, it is quite applicable [21] In current study, ‘‘hard constraint” method was implemented Indeed, this method considers additional information as a hard constraint that sets absolute boundaries on how far the final answer may deviate from the initial model In the model-based inversion algorithm, average block size and the number of iterations are of prime importance In this case, using average block size greater than seismic sample interval is a must [21] The logic behind these recommendations is the assumption that there might be some false recorded readings from surrounded environment that are possibly mixed with the data A larger number of iteration results in better accuracy, even though it is time consuming process By investigating 3D seismic data from Dalan formation, the following results were obtained; constraint limit was 25%, average block size was ms, and number of iterations was set to 10 The results of model-based inversion demonstrate that this type of modeling can clearly identify considered geological layers (Fig 8) 3.2 Constructing a 3D model of FZI In building a 3D model of FZI, the most important step is the determination of seismic attributes number In this study, the appropriate seismic attributes were selected by stepwise regression and cross validation techniques The stepwise regression Fig 10 The multi-attribute analysis result shows the average RMS error; the optimum operator length is equal to Please cite this article in press as: M Iravani et al., Application of Seismic Attribute Technique to estimate the 3D model of Hydraulic Flow Units: A case study of a gas field in Iran, Egypt J Petrol (2017), http://dx.doi.org/10.1016/j.ejpe.2017.02.003 M Iravani et al / Egyptian Journal of Petroleum xxx (2017) xxx–xxx Fig 11 Measured FZI logs (in black) and the predicted ones from the multi-attribute analysis (in red); the correlation of the training data is 0.88 Fig 12 Measured FZI logs (in black) and predicted ones (in red); the correlation of validation data is 0.66 Please cite this article in press as: M Iravani et al., Application of Seismic Attribute Technique to estimate the 3D model of Hydraulic Flow Units: A case study of a gas field in Iran, Egypt J Petrol (2017), http://dx.doi.org/10.1016/j.ejpe.2017.02.003 M Iravani et al / Egyptian Journal of Petroleum xxx (2017) xxx–xxx Table The results of the multi-attribute analysis Volume type FZI Train data Validation data Correlation Error Correlation Error 0.88 29.9 mm 0.66 49.8 mm Fig 13 FZI and AI volume slice below K4 member of Dalan horizon at 1650 ms method divides dataset into two parts that are training dataset (original wells, black line in Fig 9) and validating dataset (predicted data, red line in Fig 9) As shown in Fig 9, the horizontal axis represents the number of attributes used in the prediction and the vertical axis is the corresponding root-mean-squared prediction error This figure demonstrates that the minimum validating error occurs when only attributes are used The used seismic attributes and their corresponding prediction errors and correla- tion coefficients are listed in Table So, seismic attributes consist of acoustic impedance (AI), quadrature trace, amplitude envelope, filter 15/20–25/30, filter 45/50–55/60, filter 55/60–65/70, and cosine of instantaneous phase were used in this study to estimate flow zone indicator Foregoing filters are frequency internal attribute that extracted from raw 3D cube seismic Each row is related to a particular multi-attribute and the successive row accumulates the previous attributes The value of amplitude envelope attribute Please cite this article in press as: M Iravani et al., Application of Seismic Attribute Technique to estimate the 3D model of Hydraulic Flow Units: A case study of a gas field in Iran, Egypt J Petrol (2017), http://dx.doi.org/10.1016/j.ejpe.2017.02.003 M Iravani et al / Egyptian Journal of Petroleum xxx (2017) xxx–xxx Fig 14 FZI and AI volume slice below K4 member of Dalan horizon at 1670 ms is dependent on phase and correlated directly with the change in acoustic impedance In addition, the instantaneous phase indicator represents the continuation of the layers The instantaneous phase is an effective attribute in highlighting discontinuities on seismic and detection of reflectors, faults, pinch-outs, angularities, and bed interfaces [16] AI attribute indicates reservoir properties such as porosity, permeability, and FZI There is a reverse relationship between AI and FZI FZI is defined as the relationship between the volumetric proportions of pore space to its geometric distribution On the other hand, acoustic impedance is a function of both density and velocity Since deeper layers are harder and denser, velocity increases with depth Moreover, reduction in wave velocities and density resulted from porosity and fluid saturation is leading to decrease in AI Such a correlation can be improved by applying the residual time-shift between the target FZI logs and the seismic data The length parameter is used to resolve the difference between the frequency content of the target log and seismic attribute because the log data have high frequency and seismic data have low frequency with some specified length to relate a group of neighbouring seismic samples around the point target log (FZI) In fact, this parameter estimates target log (FZI) by using an average weight of a group of seismic samples on each attribute So, the parameter operator length determines the length of the convolution operator In this study, the optimum value of operator length was determined as high as Fig 10 shows where the number of seismic attribute is 7, the optimum value of operator length is where the average error is minimum After determining the optimum number of attributes and length operator, an equation was found between target log (FZI) and seismic attributes in well locations Fig 11 demonstrates, for the 7th attribute and an eight-point convolution operator, the correlation exponent and RMS error between the predicted FZI and actual FZI log in well location are 88% and 29.9 mm respectively for the training data Fig 12 shows the target log in black versus the ‘‘predicted” log in red for each well for validation data Finally, relationship between multi-attribute features and the target log was used to predict FZI in inter-well spaces by using seismic data Results obtained from multi-attribute analysis are shown in Table Please cite this article in press as: M Iravani et al., Application of Seismic Attribute Technique to estimate the 3D model of Hydraulic Flow Units: A case study of a gas field in Iran, Egypt J Petrol (2017), http://dx.doi.org/10.1016/j.ejpe.2017.02.003 10 M Iravani et al / Egyptian Journal of Petroleum xxx (2017) xxx–xxx Fig 15 FZI and AI volume slice, below K4 member of Dalan horizon at 1700 ms Fig 16 Cumulative frequency of FZI and optimal number of HFU categories Please cite this article in press as: M Iravani et al., Application of Seismic Attribute Technique to estimate the 3D model of Hydraulic Flow Units: A case study of a gas field in Iran, Egypt J Petrol (2017), http://dx.doi.org/10.1016/j.ejpe.2017.02.003 M Iravani et al / Egyptian Journal of Petroleum xxx (2017) xxx–xxx Table FZI ranges in each set of HFU RQI ¼ 0:0314 HFU category FZI range HFU1 HFU2 HFU3 HFU4 HFU5 ————————— < Log FZI 0.9674 0.9674 < Log FZI 0.15598 0.155979 < Log FZI 1.77864 1.77864 < Log FZI 2.52756 2.52756 < Log FZI ——————— 10000 R² = 0.7932 1000 R² = 0.964 R² = 0.8755 100 R² = 0.9602 10 K (MD) HFU1 R² = 0.6518 HFU2 0.1 HFU3 0.01 HFU4 0.001 HFU5 0.0001 0.00001 0.1 0.2 0.3 0.4 PHI(V/V) Fig 17 Plot of permeability against porosity in each category of HFUs 10 HFU RQI HFU ð3Þ where k is permeability in milli-Darcy and u is the fractional porosity The FZI can be rearranged in terms of the measurable RQI as given below: FZI ẳ RQI uz 4ị Rocks with a narrow range of FZI values belong to a single hydraulic unit, i.e., they have similar flow properties [13] In this study, optimal number of HFU categories, which depends on tact of user, is determined by analyzing break point in the plot of cumulative frequency of FZI for both SP5 and SP9 (Fig 16) The ranges of FZI in each set of HFU are shown in Table Fig 17 plots the semi-logarithmic permeability against porosity There is a good agreement between permeability and porosity in each defined HFUs It can be seen that porosity and permeability of reservoir is increasing with respect to HFU categories HFU1 demonstrates low quality reservoir with low amount of permeability and porosity whereas HFU5 represents a high quality reservoir with large amount of permeability and porosity Fig 18 displays the RQI versus pore to matrix volume ratio in each set of HFUs throughout the reservoir of the gas Field A A combination of indexes which are derived in previous section to calculate the FZI, are utilized in order to construct a 3D model of HFU Since the acoustic impedance and FZI have a reverse relation we conclude that increase in acoustic impedance results in decrease in HFUs and vice versa HFUs volume slice of reservoir at various time are illustrated in Fig 19 Results and discussion HFU 0.1 HFU 0.01 HFU HFU 0.01 0.1 HFU HFU øz Fig 18 RQI versus Uz in each category of HFUs throughout the reservoir Figs 13–15 show the comparison between FZI and AI resulted from multi-attribute analysis These figures demonstrates that an increase in AI causes decrease in the FZI 3.3 Determination of HFU HFUs are defined as zones within a reservoir having the potential to control the fluid flow Each flow unit is characterized by a flow zone indicator, which can be understood in terms of the relationship between the volume of void space, £z , and the geometric distribution of pore space (quantified as the reservoir quality index, RQI) as follows [10]: logRQIị ẳ logFZIị ỵ loguz ị 1ị where uz ẳ u 11 HFU 0.001 0.001 sffiffiffiffiffiffiffiffiffiffiffi K u 1u and u is the effective porosity RQI can be calculated using the following equations: ð2Þ In this study, the correlation exponent and RMS error between the predicted FZI and actual FZI log in well location are 88% and 29.9 mm respectively for the training data The resulted 3D FZI indicates high anomalies, which are in good agreement with the petrophysical properties of oil producing wells in the field of interest Also, optimal number of HFU categories is determined using the plot of cumulative frequency of FZI for both SP5 and SP9 HFU1 demonstrates low quality reservoir with low amount of permeability and porosity whereas HFU5 represents a high quality reservoir with large amount of permeability and porosity For inter-well spaces, where well logs and core data were not available, the intelligent model is applied In this field, from NE to SW, quality of reservoir has been increased significantly and directional wells can be drilled in deeper interval of reservoir to produce more gas Since HFU depends on the geological properties of the rocks, it helps in tracking hydrocarbon saturation changes over the Dalan and provides information respecting the locations of perforation and development wells Conclusion In this study, multi-attribute analysis is successfully used to estimate FZI log from seismic attributes This technique is a fast method to evaluate the reservoir characteristics as well as to reduce cost and increase rate of success in hydrocarbon exploration The result of this study showed that multi-attribute analysis was effective to predict HFU deploying seismic This model can be improved by neural network to be used as a guide model Distribution of producible hydrocarbon zones along with the seismic lines around the reservoir was characterized by introducing this model which can help us in choosing the location of new wells and more economical drilling operations Please cite this article in press as: M Iravani et al., Application of Seismic Attribute Technique to estimate the 3D model of Hydraulic Flow Units: A case study of a gas field in Iran, Egypt J Petrol (2017), http://dx.doi.org/10.1016/j.ejpe.2017.02.003 12 M Iravani et al / Egyptian Journal of Petroleum xxx (2017) xxx–xxx Fig 19 Time slice from 3D HFU resulted at 1650, 1670 and 1700 MS, respectively References [1] A.R Brown, et al., Interpretation of three-dimensional seismic data, 1996 [2] J Calderon, J Castagna, Porosity and lithologic estimation using rock physics and multi-attribute transforms in Balcon Field, Colombia, The Leading Edge 26 (2) (2007) 142–150 [3] D.P Hampson, J.S Schuelke, J.A Quirein, Use of multiattribute transforms to predict log properties from seismic data, Geophysics 66 (1) (2001) 220–236 [4] D.J Leiphart, B.S Hart, Comparison of linear regression and a probabilistic neural network to predict 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as: M Iravani et al., Application of Seismic Attribute Technique to estimate the 3D model of Hydraulic Flow Units: A case study of a gas field in Iran, Egypt... gas field in the southwest of Iran were used in this study Study area Fig Geographical location of the gas field A The gas field A, is one of the largest gas accumulations in the world containing