Estimation of the non records logs from existing logs using artificial neural networks

Salehi, Mehdi; Rahmati, Mehdi; Karimnezhad, Masoud; Omidvar, Pouria

doi:10.1016/j.ejpe.2016.11.002

Cited by 57 publications

(22 citation statements)

References 10 publications

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“…Later, the importance of BP was appreciated and a faster work flow was documented by Rumelhart et al (1986). The aim of the supervised learning algorithm (BP) is to repeatedly adjust the weights by connecting the neurons of different layers in such a way that the neural network can map out an arbitrary nonlinear relationship between the inputs and outputs (Fausett 1993;Salehi et al 2017). The simplest network consists of one input, one hidden and one output layer (Rogers et al 1992;Benaouda et al 1999).…”

Section: Mlf Neural Networkmentioning

confidence: 99%

“…The petrophysical parameters of the rocks are empirically or sometimes directly related to the reservoir parameters (Serra 1984;Schlumberger 1989). In complex geological setup, the ability to obtain reliable and accurate reservoir properties is crucial (Mohaghegh 2000), and most of the researchers have used the AI techniques (Fung et al 1997;Wang et al 2013;Singh et al 2015;Salehi et al 2017). This technique has the remarkable ability in establishing a complicated mapping between the nonlinearly linked input and output data (Nakutnyy et al 2008).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Prediction of reservoir parameters in gas hydrate sediments using artificial intelligence (AI): A case study in Krishna–Godavari basin (NGHP Exp-02)

Mukherjee

Sain

2019

J Earth Syst Sci

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The estimation of accurate reservoir parameters is essential for conventional and non-conventional hydrocarbon prospects. An artificial neural network has been developed to predict the reservoir parameters (porosity and saturation of gas hydrates) in a silty-sand, sandy-silt and pelagic-poor clay reservoir at two neighbour wells using the petrophysical information at another well in the Krishna-Godavari basin. The well log data were acquired during the Expedition-02 of Indian National Gas Hydrates Program (NGHP Exp-02). The estimation of gas hydrate saturation using Archie's equation may be erroneous, as it is valid for the quantification of conventional hydrocarbons in the clean sand reservoir. Since the study area is clay dominated, it is subjective to adjust Archie's exponents so that it matches with the saturation, measured from the core data. To overcome this problem of estimating the reservoir parameters in such a scenario, first of all we have derived porosity from the density log data and estimated saturation by employing modified Archie's equation to the resistivity log data at one well. In order to train the network, the log data at one well are taken as inputs and corresponding porosity and saturation are taken as outputs. The reservoir parameters are then predicted at two neighbour wells using the wireline log data as input in those two wells. The predicted porosity and saturation of gas hydrates are alike to the traditionally estimated porosity and saturation at the neighbour wells. The predicted porosity in the studied region varies between 33 and 76%, whereas the saturation of gas hydrates ranges between 3.39 and 86.92%. This shows that the designed network can be used to estimate the reservoir parameters directly from the well log data in the same reservoirs.

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Section: Mlf Neural Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of reservoir parameters in gas hydrate sediments using artificial intelligence (AI): A case study in Krishna–Godavari basin (NGHP Exp-02)

Mukherjee

Sain

2019

J Earth Syst Sci

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“…In these cases, a solution is to acquire additional well log data by new drilling or by rerunning the well logging to obtain the required log type for an already drilled well. However, drilling a new well or stopping production to rerun logging causes a huge additional cost, and some log types are not measurable owing to casing [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

“…Salehi et al trained a deep neural network (DNN) with three hidden layers using two wells from a carbonate oil reservoir on the southwest of Iran [2]. The target field consisted of eight pays, but they chose one pay zone to generate the prediction models because each pay had different lithology.…”

Section: Introductionmentioning

confidence: 99%

“…If the goal is to construct a reliable porosity model and a target field lacks sonic log, a prediction model for sonic log should be trained with the available log data. However, previous prediction models tried to make a prediction model without considering which logs are needed for the prediction and why [2,3,6]. Therefore, a trained model required numerous well logs for the input layer and hundreds of wells were used for training [5,7].…”

Section: Introductionmentioning

confidence: 99%

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Generation of Synthetic Density Log Data Using Deep Learning Algorithm at the Golden Field in Alberta, Canada

Kim

Min

et al. 2020

Geofluids

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This study proposes a deep neural network-(DNN-) based prediction model for creating synthetic log. Unlike previous studies, it focuses on building a reliable prediction model based on two criteria: fit-for-purpose of a target field (the Golden field in Alberta) and compliance with domain knowledge. First, in the target field, the density log has advantages over the sonic log for porosity analysis because of the carbonate depositional environment. Considering the correlation between the density and sonic logs, we determine the sonic log as input and the density log as output for the DNN. Although only five wells have a pair of training data in the field (i.e., sonic and density logs), we obtain, based on geological knowledge, 29 additional wells sharing the same depositional setting in the Slave Point Formation. After securing the data, 5 wells among the 29 wells are excluded from dataset during preprocessing procedures (elimination of abnormal data and min-max normalisation) to improve the prediction model. Two cases are designed according to usage of the well information at the target field. Case 1 uses only 23 of the surrounding wells to train the prediction model, and another surrounding well is used for model testing. In Case 1, the Levenberg-Marquardt algorithm shows a fast and reliable performance and the numbers of neurons in the two hidden layers are of 45 and 14, respectively. In Case 2, the 24 surrounding wells and four wells from the target field are used to train the DNN with the optimised parameters from Case 1. The synthetic density logs from Case 2 mitigate an underestimation problem in Case 1 and follow the overall trend of the true density logs. The developed prediction model utilises the sonic log for generating the synthetic density log, and a reliable porosity model will be created by combining the given and the synthetic density logs.

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Identifying Applications of Machine Learning and Data Analytics Based Approaches for Optimization of Upstream Petroleum Operations

2020

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In the mid-19th century, the first oil well was drilled, which marked the beginning of the world oil economy. This era embarked on the phase of evolution and development of society in every aspect, with huge dependence on petroleum products, which subsequently led to tremendous growth in terms of technological advancements in the exploration and production activities over time. With the existing petroleum fields approaching toward depletion, the global need for energy resources is increasing simultaneously. This continuously rising demand has led us to move to complex environments for oil and gas exploration and production activities. Moreover, the real-time monitoring of reservoirs and well operations produces enormous data, which require efficient processing and analysis tools for decisionmaking regarding field development and asset management. [1,2] Subsequently, the need for optimum facilities design, [3] transportation [4] and refining [5] activities at reduced cost has also grown. Several attempts [6-9] are also being made to develop unconventional fields commercially based on technoeconomic viability. The implementation of computer-based techniques has proven to be promising toward many challenging problems for various oilfield operations. Several researchers have shown that the machine learning (ML) and data analytics (DA) approaches hold promising solutions toward the operational challenges being encountered at present in the industry and can efficiently help in resolving the issues related to interpretation and analysis of large datasets. [10-15] Nikravesh and Aminzadeh [16] have shown the use of neural networks (NNs) and fuzzy logic for mining petroleum data. Li and Li [17] combined a NN and cluster analysis to put forth a predictive model for identifying complex lithology. Fath et al. [18] proposed a novel approach for bubble point pressure prediction using an NN model with reservoir temperature, solution gas oil ratio (GOR), oil gravity, and gas specific gravity as input attributes. Ahmadi and Bahadori [19] showed the use of a supervised learning algorithm to determine the well placement and conning occurrence in horizontal wells. Maucec et al. [20] performed data mining and ML on well stimulation data for enhancing the prediction capabilities. Gupta et al. [21] applied DA for safeguarding real-time electrical submersible pumping operations. Cadei et al. [22] forecasted operational upsets using advanced analytics in an upstream production system. Dongxiao et al. [23] presented in their research that long short-term memory (LSTM), cascaded LSTM, and a fully connected NN can be utilized for generating synthetic well logs by supplementing the missing logging input data. Ghorbani et al. [24] proposed an artificial Intelligence (AI)based approach to predict the flow rate of oil from an orifice

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Estimation of the non records logs from existing logs using artificial neural networks

Cited by 57 publications

References 10 publications

Prediction of reservoir parameters in gas hydrate sediments using artificial intelligence (AI): A case study in Krishna–Godavari basin (NGHP Exp-02)

Prediction of reservoir parameters in gas hydrate sediments using artificial intelligence (AI): A case study in Krishna–Godavari basin (NGHP Exp-02)

Generation of Synthetic Density Log Data Using Deep Learning Algorithm at the Golden Field in Alberta, Canada

Identifying Applications of Machine Learning and Data Analytics Based Approaches for Optimization of Upstream Petroleum Operations

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