Oil Reservoir Properties Estimation Using Neural Networks

Journal of Applied Computing Research, 2011

Due to limited understanding of many diagenetic processes which contributes to petroleum quality determination, mathematical models become a very useful tool to improve understanding of these processes and to improve reservoir quality predictions prior drilling. Especially for reservoir engineers and petrophysicists the distribution of porosity and permeability are very important in the formation evaluation and definition of recovery strategies and evaluation of reservoir quality. In this context, we have developed an artificial neural network based model to predict macroporosity of sandstones reservoir systems. We have used a score to quantify the importance of each feature in prediction process. This score allows creating progressive enhancement neural models, which are simpler and more accurate than conventional neural network models and multiple regressions. The main contribution of this paper is the building of a reduced model just with the most relevant features to macroporosity prediction. A dataset, containing petrographic and petrophysical characteristics, containing samples of the same formation sandstone reservoir was investigated. Study results show that progressive enhancement neural network is able to predict macroporosity with accuracy near 90%, suggesting that this technique is a valuable tool for reservoir quality prediction.

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.

2008

The Artificial Neural Network (ANN) is a functional imitation of simplified model of the biological neurons and their goal is to construct useful computers for real-world problems and reproduce intelligent data evaluation techniques like: Pattern recognition, classification and generalization by using simple, distributed and robust processing units called artificial neurons. ANNs are fine-grained parallel implementation of non -linear static-dynamic systems.

2015

Porosity is one of the most significant parameters of hydrocarbon reservoirs describing the quality of reservoirs rocks. It is one of the most crucial characteristics that need to be predicted for evaluation of reservoirs. The conventional methods for porosity determination are core analysis and well test technique. These methods are however very expensive and time-consuming tasks. One of the comparatively inexpensive and readily available sources of inferring porosity is nuclear magnetic resonance (NMR) log. The aim of this paper is to present an application of two machine learning methodologies, which are christened general regression neural network (GRNN) and back-propagation neural network (BPNN), for prediction of NMR porosity using well log data and intelligent models. Available data of three was considered for training and testing the networks. Verification process was also performed by one remaining well. Obtained results have shown that the overall correlation coefficients ...

2019

Well Drilling costs a lot without knowing porosity distribution. Geoscientists use the seismic waves to overcome this problem and reduce the exploration risk. The current paper proposes a system to predict porosity of well from other wells already drilled incorporating with seismic data. This proposed workflow aims to estimate porosity values from three-dimensional seismic data and wells records from F3-block North Sea data. We used porosity interpretations from two wells (F2-1 and F3-2) and three-dimensional seismic attributes for neural network training. for assessing the result of porosity prediction, we used data from another well (F3-4) as a blind well. Correlation in the three stages of training, validation, and testing are discussed. Test results indicate the superiority of the proposed Neural Network to predict porosity compared to other techniques in current use. By implementing Neural Network to predict porosity in blind well it is found that correlation R=0.98.

All Days, 2009

Analysis of heterogeneous gas sand reservoirs is one of the most difficult problems. These reservoirs usually produce from multiple layers with different permeability and complex formation, which is often enhanced by natural fracturing. Therefore, using new well logging techniques like NMR or a combination of NMR and conventional openhole logs, as well as developing new interpretation methodologies are essential for improved reservoir characterization. Nuclear magnetic resonance (NMR) logs differ from conventional neutron, density, sonic and resistivity logs because the NMR measurements provide mainly lithology independent detailed porosity and offer a good evaluation of the hydrocarbon potential. NMR logs can also be used to determine formation permeability and capillary pressure. In heterogeneous reservoirs classical methods face problems in determining accurately the relevant petrophysical parameters. Applications of artificial intelligence have recently made this challenge a pos...

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...

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...

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.

Journal of Petroleum and Mining Engineering, 2020

Petrophysical properties evaluation of shaly sandstone reservoirs is a challenging task in comparison to clean sand reservoirs. Logging derived porosity in shaly sands requires shale correction and Archie's formula cannot be used in shaly sands for the determination of water saturation, therefore many water saturation models were proposed to get accurate water saturation of shaly sand reservoirs. In this paper, three water saturation models were used; two empirical models (Simandoux and total shale) and one theoretical model (effective medium model). Shale corrected density log was used in all models. The use of computer-generated algorithm, fuzzy log neural network is of increasing interest in the petroleum industry. This paper presents artificial neural network (ANN) as an effective tool for determining porosity and water saturation in shaly sand reservoir using well logging data. ANN technique utilizes the prevailing unknown nonlinear relationship in data between input logging data and output petrophysical parameters. Results of this work showed that ANN can be supplement or replacement of the existing conventional techniques to determine porosity and water saturation using empirical or theoretical water saturation models. Two neural networks were presented to determine porosity and water saturation using GR, resistivity and density logging data and adapted cut off for porosity and water saturation. Water saturation and porosity were determined using conventional techniques and neural network approach for two wells in a shaly sand reservoir. Neural network approach was trained for porosity and water saturation using the available well logging data. The predicted porosity and water saturation values have shown good matching with the core data in the two wells in comparison to the porosity and water saturation derived from the conventional techniques. This work showed that developed neural network (ANN) could provide an accurate porosity and water saturation in shaly sands reservoirs, it is subject to volume of available well logging data.