Hydrocarbon Prediction Using Seismic Multi-Attributes and Probabilistic Neural Network from Log Data in Gas Sands Reservoir (original) (raw)
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Proceedings The 38th HAGI & 42nd IAGI Joint Convention & Exhibition Medan, 2013
Azuni Field is located on South Sumatra Basin was discovered in 2004 and since 2006 this field producing oil from Lower Talang Akar Formation (LTAF). This field also has potential gas reservoir in Gumai Formation (GUF). There are two main potential gas reservoirs in this formation, Sand-11 and Sand-12 which are potentially to be developed in the next phase. The Gumai sand formation is relatively thick that represents pro-deltaic environments depositional system. The seismic section over this interval indicate alternating peak and through which may indicate the presence of sand reservoir layers alternating with shale. To generate Gumai reservoir distribution map of Sand-11 and Sand-12, probabilistic neural network (PNN) analysis based on multi-attribute utilizing 3D PSTM seismic data and 14 wells has been applied. Based on sensitivity analysis, sands and shales which representing reservoir and non-reservoir can be differentiated from density log. A statistical approach, including multi-attribute and PNN analysis were used to derive the relationship between attributes of the seismic data and density log. The established relationship was used to estimate pseudo density at each seismic trace on the 3-D. The results of PNN analysis indicated that four attributes show high correlation 0.86 of log density properties in the study area. This method provides the distribution and orientation of reservoir targets consistent with well data. It has been successfully used to define the distribution of Sand-11 and Sand-12 of GUF. In future this study result will advance the petroleum development activities in the area.
Earth Science Informatics, 2021
The Prediction of the reservoir characteristics from seismic amplitude data is a main challenge. Especially in the Nile Delta Basin, where the subsurface geology is complex and the reservoirs are highly heterogeneous. Modern seismic reservoir characterization methodologies are spanning around attributes analysis, deterministic and stochastic inversion methods, Amplitude Variation with Offset (AVO) interpretations, and stack rotations. These methodologies proved good outcomes in detecting the gas sand reservoirs and quantifying the reservoir properties. However, when the pre-stack seismic data is not available, most of the AVO-related inversion methods cannot be implemented. Moreover, there is no direct link between the seismic amplitude data and most of the reservoir properties, such as hydrocarbon saturation, many assumptions are imbedded and the results are questionable. Application of Artificial Neural Network (ANN) algorithms to predict the reservoir characteristics is a new eme...
Journal of Petroleum Exploration and Production Technology, 2018
Supervised multilayer perceptron neural network and seismic multiattribute transforms were applied to three-dimensional (3D) seismic and a suite of borehole log data set obtained from Pennay field, offshore Niger Delta with a view to predicting lateral continuity of hydrocarbon reservoir properties beyond well control. Four (4) hydrocarbon-bearing sands, namely, Pennay 1, 2, 3, and 4, were delineated from borehole log data. Four (4) horizons corresponding to near top of mapped hydrocarbon-bearing sands were used to produce time maps and then depth structural maps using appropriate checkshot data. Petrophysical analysis of the mapped reservoirs revealed that the area is characterized with hydrocarbon saturation ranging from 56 to 72%, water saturations between 27 and 44%, volume of shale between 7 and 20%, and porosity between 25 and 31%. Three major structure building faults (F2, F3, and F5 which are normal, listric concave in nature), two antithetic (F1 and F4), were identified. Structural closures identified as roll-over anticlines and displayed on the time and depth structure maps suggest probable hydrocarbon accumulation at the upthrown side of the fault F4. Seismic attribute results reveal two main characteristic patterns of high and low amplitude and frequency areas. There is an intermediate zone between the low and high amplitude and frequency that may be regarded as transition zone. Multilayer Perceptron Neural Networks (MLPNN) revealed how permeability, net-to-gross, porosity, volume of shale, and hydrocarbon saturation vary away from well control across the entire field. The supervised MLPNN-simulated volumes for petrophysical properties of interest have their uncertainties quantified and measure of accuracy in prediction and the predictive power in terms of root-mean-square error (RMSE). RMSE is the standard deviation of residual which is difference between the predicted values and observed values, i.e., input and output of the network. RSME ranges between 0 and 1 and values closer to zero (0) indicate a fit that is more useful for prediction and also very high confidence level for the resulting output. Permeability modelled at RMSE of 0.030 revealed some thief zones (channel with high absolute permeability) within and outside the areas with well concentration with average permeability of 635md. MLPNN-modelled map of net-to-gross (NTG) at RMSE of 0.0290 revealed that 72% of the reservoirs have a very high NTG (ratio of the volume of the sand in the reservoir to the total volume of the reservoir) with average NTG of 0.7184. Effective porosity was modelled at RMS error of 0.0053 with resulting average effective porosity of 0.295. The effective porosity slice on top of reservoirs revealed the lateral variation of effective porosity across the field with very high effective porosity areas coincide with the delineated regions of interests. Moreover, hydrocarbon saturation was also modelled at RMSE of 0.0282 with an average of 69.7% and volume of shale was modelled at RMSE of 0.028 and average volume of shale of 9%. A comparison of MLPNN slices of petrophysical properties on the mapped horizons revealed that a relatively higher NTG, low volume of shale, high hydrocarbon saturation, high permeability, and high effective porosity were observed in the regions of interest in the field. In conclusion, successful MLPNN prediction has been done for petrophysical properties at inter-well points and locations beyond well control. MLPNN-modelled maps revealed some bypassed sand channels and some thief zones within and outside the areas with well concentration that are not evident on the structural maps and attribute slices. The integration of the different analyses and results from the study has improved our understanding of mapped reservoirs and enhanced lateral prediction of its properties.
Iraqi Journal of Science, 2021
The EMERGE application from Hampsson-Russell suite programs was used in the present study. It is an interesting domain for seismic attributes that predict some of reservoir three dimensional or two dimensional properties, as well as their combination. The objective of this study is to differentiate reservoir/non reservoir units with well data in the Yamama Formation by using seismic tools. P-impedance volume (density x velocity of P-wave) was used in this research to perform a three dimensional seismic model on the oilfield of Nasiriya by using post-stack data of 5 wells. The data (training and application) were utilized in the EMERGE analysis for estimating the reservoir properties of P-wave velocity, in addition to the neural network analysis and deriving relations between them at well locations. P- wave velocity slices of reservoir units (Yb1, Yb2, and Yc) of Yamama Formation were prepared to determine the enhancement trends within these units. From a general economic poi...
Applied Sciences
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2015
Lithofacies identification can provide qualitative information about rocks. It can also explain rock textures which are important components for hydrocarbon reservoir description Sarvak Formation is an important reservoir which is being studied in the Marun oil field, in the Dezful embayment (Zagros basin). This study establishes quantitative relationships between digital well logs data and routine petrographic data, obtained from thin sections description. Attempts were made to predict lithofacies in 13 wells, all drilled in the Marun oil field. Seven well logs, namely, Gamma Ray (SGR and CGR), Deep Resistivity (RD), Formation Density (RHOB), Neutron Porosity (PHIN), Sonic log (DT), and photoelectric factor (PEF) as input data and thin section/core-derived lithofacies were used as target data in the ANN (artificial neural network) to predict lithofacies. The results show a strong correlation between the given data and those obtained from ANN (R²= 95%). The performance of the model has been measured by the Mean Squared Error function which doesn't exceed 0.303. Hence, neural network techniques are recommended for those reservoirs in which facies geometry and distribution are key factors controlling the heterogeneity and distribution of rock properties. Undoubtedly, this approach can reduce uncertainty and save plenty of time and cost for the oil industry.
Neural networks in petroleum geology as interpretation tools
Central European Geology, 2010
Three examples of the use of neural networks in analyses of geologic data from hydrocarbon reservoirs are presented. All networks are trained with data originating from clastic reservoirs of Neogene age located in the Croatian part of the Pannonian Basin. Training always included similar reservoir variables, i.e. electric logs (resistivity, spontaneous potential) and lithology determined from cores or logs and described as sandstone or marl, with categorical values in intervals. Selected variables also include hydrocarbon saturation, also represented by a categorical variable, average reservoir porosity calculated from interpreted well logs, and seismic attributes. In all three neural models some of the mentioned inputs were used for analyzing data collected from three different oil fields in the Croatian part of the Pannonian Basin. It is shown that selection of geologically and physically linked variables play a key role in the process of network training, validating and processing. The aim of this study was to establish relationships between log-derived data, core data, and seismic attributes. Three case studies are described in this paper to illustrate the use of neural network prediction of sandstone-marl facies (Case Study # 1, Okoli Field), prediction of carbonate breccia porosity (Case Study # 2, Benićanci Field), and prediction of lithology and saturation (Case Study # 3, Kloštar Field). The results of these studies indicate that this method is capable of providing better understanding of some clastic Neogene reservoirs in the Croatian part of the Pannonian Basin.
2013
This study focuses on one of the oil fields located east of Kalol main field in Nardipur Low area of Cambay Basin. The major reservoir unit in the interested area consists of mainly alternating coal, silt, shale and occasionally fine to medium grained sands as channel fills, crevasse splay deposited in lower delta plain environment. In the available 3D seismic data, the coal layer occurring at the top part of Kalol-IX litho unit and the pay sand capped by thin shale layer below the coal layer are not resolved and generate a composite detectable seismic response, making reservoir characterization difficult through conventional seismic attribute analysis.
geopersia, 2015
Lithofacies identification can provide qualitative information about rocks. It can also explain rock textures which are importantcomponents for hydrocarbon reservoir description Sarvak Formation is an important reservoir which is being studied in the Marun oilfield, in the Dezful embayment (Zagros basin). This study establishes quantitative relationships between digital well logs data androutine petrographic data, obtained from thin sections description. Attempts were made to predict lithofacies in 13 wells, all drilled inthe Marun oil field. Seven well logs, namely, Gamma Ray (SGR and CGR), Deep Resistivity (RD), Formation Density (RHOB),Neutron Porosity (PHIN), Sonic log (DT), and photoelectric factor (PEF) as input data and thin section/core-derived lithofacies wereused as target data in the ANN (artificial neural network) to predict lithofacies. The results show a strong correlation between the givendata and those obtained from ANN (R²= 95%). The performance of the model has bee...