COMPARISON OF FRACTIONAL NOISE DISTRIBUTIONS FOR GENERATING HETEROGENEOUS PERMEABILITY FIELDS IN OIL RESERVOIR SIMULATIONS: A CASE STUDY

Fractals ◽  
2018 ◽  
Vol 26 (04) ◽  
pp. 1850066
Author(s):  
MARYAM GHORBANI ◽  
MOHAMMAD REZA KHORSAND MOVAGHAR
Keyword(s):  
Hurst Exponent ◽  
Reservoir Rock ◽  
Rock Properties ◽  
Heterogeneous Reservoirs ◽  
Regionalized Variables ◽  
Geostatistical Models ◽  
Permeability Distribution ◽  
Simulation Results ◽  
Core Permeability ◽  
Precise Simulation

Prediction of reservoir rock properties, especially permeability distribution is needed for precise simulation of heterogeneous reservoirs. Interwell permeability fields have recently been considered for dynamic simulation using geostatistical models and fractal geometries. The geostatistical models employ experimentally observed variograms to characterize the spatial variability of regionalized variables such as permeability. Fractal models can be useful in assessing the spatial correlation of a property because their variogram can be characterized with a single parameter called the Hurst exponent. In this study, based on core permeability data of each well, Hurst exponent (using [Formula: see text] analysis) is assigned locally to each well by means of stream lines and as averaged value for interwell spaces. Then, permeability distributions are created using Fractional Brownian Motion (FBM) and Fractional Gaussian Noise (FGN) models by implementing fast Fourier transform (FFT). Through comparison between simulation results of these models, as well as real grid simulation results, the averaged distribution was shown to give better results over a locally assigned fractal distribution. Furthermore, predictions of field pressure using the FGN model were shown to function better than the FBM model for vertical wells.

P- and S-wave attenuation logs from monopole sonic data

Geophysics ◽  
2000 ◽  
Vol 65 (3) ◽  
pp. 755-765 ◽  
Author(s):  
Xinhua Sun ◽  
Xiaoming Tang ◽  
C. H. (Arthur) Cheng ◽  
L. Neil Frazer
Keyword(s):  
Wave Attenuation ◽  
Fluid Velocity ◽  
P Wave ◽  
Reservoir Rock ◽  
Rock Properties ◽  
S Wave ◽  
Intrinsic Attenuation ◽  
Modified Method ◽  
Receiver Array ◽  
Attenuation Profiles

In this paper, a modification of an existing method for estimating relative P-wave attenuation is proposed. By generating synthetic waveforms without attenuation, the variation of geometrical spreading related to changes in formation properties with depth can be accounted for. With the modified method, reliable P- and S-wave attenuation logs can be extracted from monopole array acoustic waveform log data. Synthetic tests show that the P- and S-wave attenuation values estimated from synthetic waveforms agree well with their respective model values. In‐situ P- and S-wave attenuation profiles provide valuable information about reservoir rock properties. Field data processing results show that this method gives robust estimates of intrinsic attenuation. The attenuation profiles calculated independently from each waveform of an eight‐receiver array are consistent with one another. In fast formations where S-wave velocity exceeds the borehole fluid velocity, both P-wave attenuation ([Formula: see text]) and S-wave attenuation ([Formula: see text]) profiles can be obtained. P- and S-wave attenuation profiles and their comparisons are presented for three reservoirs. Their correlations with formation lithology, permeability, and fractures are also presented.

Comprehensive study of the geological structure Verkhneyakovlev deposits of the Tagul field in the process of justifications for operational drilling

2021 ◽  
pp. 85-97
Author(s):  
A. S. Titenkov ◽  
Yu. N. Utyashev ◽  
A. A. Evdoshchuk ◽  
V. A. Belkina ◽  
D. V. Grandov
Keyword(s):  
Complex Structure ◽  
Geological Structure ◽  
Reservoir Rock ◽  
Rock Properties ◽  
Capital Costs ◽  
Complex Geological Structure ◽  
Well Correlation ◽  
Effective Development ◽  
The Cost ◽  
Available Information

Currently, most of the fields being put into development are characterized by a complex geological structure, both in terms of section and in terms of plan. The solution of all geological tasks, including such important ones as the preparation of exploration projects, operation and effective development management, is impossible without creating models that reflect the main features of the variability of target parameters. The construction of adequate models of objects with a complex structure requires the involvement of all available information. The accuracy of the geological model is mostly determined by the accuracy of the well correlation. Paleosols are a new marker for the complex-built layers of the VAk-2 and VAk-3(1) of the Tagul field, which contributes to the validity of the correlation of the section of these layers. The reliability of the model was also improved by the use of the results of facies analysis. This analysis showed that the sedimentation of the studied objects includes channel and floodplain facies. Reservoir rock properties of these facies differ significantly. The updated model is characterized by a reduction in the oil-bearing area and the amount of reserves. The implementation of the model will optimize the project fund of wells and reduce the cost of well intervention. Economically, this means reducing capital costs and increasing the profitability of the project.

scholarly journals Reservoir Performance Prediction using Integrated Production Modelling (MBAL Software)

2019 ◽  
Vol 8 (4) ◽  
pp. 1484-1489
Keyword(s):  
Performance Prediction ◽  
History Matching ◽  
Material Balance ◽  
Development Planning ◽  
Reservoir Rock ◽  
Rock Properties ◽  
Production Operation ◽  
Gas Field ◽  
Reservoir Performance ◽  
Fluid Properties

Reservoir performance prediction is important aspect of the oil & gas field development planning and reserves estimation which depicts the behavior of the reservoir in the future. Reservoir production success is dependent on precise illustration of reservoir rock properties, reservoir fluid properties, rock-fluid properties and reservoir flow performance. Petroleum engineers must have sound knowledge of the reservoir attributes, production operation optimization and more significant, to develop an analytical model that will adequately describe the physical processes which take place in the reservoir. Reservoir performance prediction based on material balance equation which is described by Several Authors such as Muskat, Craft and Hawkins, Tarner’s, Havlena & odeh, Tracy’s and Schilthuis. This paper compares estimation of reserve using dynamic simulation in MBAL software and predictive material balance method after history matching of both of this model. Results from this paper shows functionality of MBAL in terms of history matching and performance prediction. This paper objective is to set up the basic reservoir model, various models and algorithms for each technique are presented and validated with the case studies. Field data collected related to PVT analysis, Production and well data for quality check based on determining inconsistencies between data and physical reality with the help of correlations. Further this paper shows history matching to match original oil in place and aquifer size. In the end conclusion obtained from different plots between various parameters reflect the result in history match data, simulation result and Future performance of the reservoir system and observation of these results represent similar simulation and future prediction plots result.

Application of Computational Intelligence in Generating Synthetic Reservoir Rock Mechanical Parameters for Building Geo-Models.

2015 ◽  
Author(s):  
L. C. Akubue ◽  
A.. Dosunmu ◽  
F. T. Beka
Keyword(s):  
Neural Network ◽  
Statistical Analysis ◽  
Numerical Models ◽  
Petroleum Industry ◽  
Oil Field ◽  
Wellbore Stability ◽  
Reservoir Rock ◽  
Rock Properties ◽  
Earth Model ◽  
Sonic Log

Abstract Oil field Operations such as wellbore stability Management and variety of other activities in the upstream petroleum industry require geo-mechanical models for their analysis. Sometimes, the required subsurface measurements used to estimate rock parameters for building such models are unavailable. On this premise, past studies have offered variety of methods and investigative techniques such as empirical correlations, statistical analysis and numerical models to generate these data from available information. However, the complexity of the relationships that exists between the natural occurring variables make the aforementioned techniques limited. This work involves the application of Artificial Neural Networks (ANNs) to generating rock properties. A three-layer back-propagation neural network model was applied predicting pseudo-sonic data using conventional wireline log data as input. Four well data from a Niger-Delta field were used in this study, one for training, one for validating and the two others for generating and testing results. The network was trained with different sets of initial random weights and biases using various learning algorithms. Root mean square error (RMSE) and correlation coefficient (CC) were used as key performance indicators. This Neural-Network-Generated-Sonic-log was compared with those generated with existing correlations and statistical analysis. The results showed that the most influential input vectors with various configurations for predicting sonic log were Depth-Resistivity-Gamma ray-Density (with correlating coefficient between 0.7 and 0.9). The generated sonic was subsequently used to compute for other elastic properties needed to build mechanical earth model for evaluating the strength properties of drilled formations, hence optimise drilling performance. The models are useful in Minimizing well cost, as well as reducing Non Productive Time (NPT) caused by wellbore instability. This technique is particularly useful for mature fields, especially in situations where obtaining this well logs are usually not practicable.

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