scholarly journals Geomechanical modeling of reservoir rock using 2D seismic inversion: Its application to wellbore stability in the onshore of Northwest Java Basin, Indonesia

2018 ◽  
Author(s):  
B. Finisha ◽  
A. Haris ◽  
I. S. Ronoatmojo
Keyword(s):  
Seismic Inversion ◽  
Wellbore Stability ◽  
Reservoir Rock

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.

3D Stress Field Modeling for Wellbore Stability Analysis

2021 ◽  
Author(s):  
Shaoxuan Li ◽  
Lei Liu ◽  
Zhilei Han ◽  
Rui Wu ◽  
Naichuan Guo
Keyword(s):  
Stability Analysis ◽  
Stress Field ◽  
High Precision ◽  
Seismic Data ◽  
Large Scale ◽  
Seismic Inversion ◽  
Modeling Method ◽  
Wellbore Stability ◽  
Bohai Bay ◽  
Oilfield Development

Abstract Borehole collapse is one of the difficult problems in offshore oilfield drilling, which is directly related to regional geostress field. Seismic inversion is widely used to construct 3D stress field, and the accuracy of seismic inversion results depends heavily on the quality of seismic data. Therefore, in the development oilfield with poor seismic data quality, we use attribute modeling method to construct high-precision 3D geostress field to help analyze the wellbore stability of oilfield development wells. First, 3D structural model is created by using seismic interpretation horizon and fault. Then, the 3D stress body is constructed by filling the vertical and horizontal attributes according to certain constraints by using the known geostress values at the wellbore of the drilled development wells. P oilfield is a large Neogene oilfield located in Bohai Bay. The serious expansion of the wellbore in the shallow unconsolidated sandstone in the oilfield area leads to high engineering risks. Moreover, due to the influence of large-scale gas cloud area, the effective wave energy on seismic profile is submerged. Through the above attribute modeling method, the high-precision 3D geostress field of P oilfield is successfully constructed. Practical application shows this method is suitable for the evaluation of geostress and wellbore stability analysis in areas with high well control in the middle and late stages of oilfield development. Especially in the area where the seismic data is not reliable enough, the 3D modeling results of this method has higher accuracy than the seismic inversion results.

scholarly journals Application of maximum likelihood and model-based seismic inversion techniques: a case study from K-G basin, India

2021 ◽  
Author(s):  
Richa ◽  
S. P. Maurya ◽  
Kumar H. Singh ◽  
Raghav Singh ◽  
Rohtash Kumar ◽  
...  
Keyword(s):  
Maximum Likelihood ◽  
Seismic Data ◽  
Acoustic Impedance ◽  
Gamma Ray ◽  
Seismic Inversion ◽  
Reservoir Rock ◽  
Well Log ◽  
Log Data ◽  
Petrophysical Parameters ◽  
Model Based

AbstractSeismic inversion is a geophysical technique used to estimate subsurface rock properties from seismic reflection data. Seismic data has band-limited nature and contains generally 10–80 Hz frequency hence seismic inversion combines well log information along with seismic data to extract high-resolution subsurface acoustic impedance which contains low as well as high frequencies. This rock property is used to extract qualitative as well as quantitative information of subsurface that can be analyzed to enhance geological as well as geophysical interpretation. The interpretations of extracted properties are more meaningful and provide more detailed information of the subsurface as compared to the traditional seismic data interpretation. The present study focused on the analysis of well log data as well as seismic data of the KG basin to find the prospective zone. Petrophysical parameters such as effective porosity, water saturation, hydrocarbon saturation, and several other parameters were calculated using the available well log data. Low Gamma-ray value, high resistivity, and cross-over between neutron and density logs indicated the presence of gas-bearing zones in the KG basin. Three main hydrocarbon-bearing zones are identified with an average Gamma-ray value of 50 API units at the depth range of (1918–1960 m), 58 API units (2116–2136 m), and 66 API units (2221–2245 m). The average resistivity is found to be 17 Ohm-m, 10 Ohm-m, and 12 Ohm-m and average porosity is 15%, 15%, and 14% of zone 1, zone 2, and zone 3 respectively. The analysis of petrophysical parameters and different cross-plots showed that the reservoir rock is of sandstone with shale as a seal rock. On the other hand, two types of seismic inversion namely Maximum Likelihood and Model-based seismic inversion are used to estimate subsurface acoustic impedance. The inverted section is interpreted as two anomalous zones with very low impedance ranging from 1800 m/s*g/cc to 6000 m/s*g/cc which is quite low and indicates the presence of loose formation.

An integrated approach to quantify geologic controls on carbonate pore types and permeability, Puguang gas field, China

2017 ◽  
Vol 5 (4) ◽  
pp. T545-T561 ◽  
Author(s):  
Qifei Huang ◽  
Qifeng Dou ◽  
Yiwei Jiang ◽  
Qingsheng Zhang ◽  
Yuefeng Sun
Keyword(s):  
Pore Structure ◽  
Spatial Variation ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Reservoir Rock ◽  
Index Formula ◽  
Accurate Estimation ◽  
High Porosity ◽  
Gas Field ◽  
Intercrystalline Porosity

Accurate estimation of permeability for reservoir simulation and production is challenging in carbonate rocks due to the diversified pore structures resulting from deposition and diagenetic modification. A significant amount of residue gas is expected in the Puguang field, China. We use a shear-frame flexibility factor [Formula: see text] from a rock-physics model as an index to quantify its spatial variation of pore structure and constrain the estimation of permeability in this field. The pore-structure index [Formula: see text] is established and used to classify various permeability-porosity trends at well locations where core and log data are available. It is found that when [Formula: see text], the pore type is dominated by intercrystalline pores with large pore-throat sizes and high connectivity, the permeability-porosity relation is [Formula: see text]; when [Formula: see text], the pore type consists of isolated moldic pores, the permeability-porosity relation is [Formula: see text], where [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] are constants, which are 80, 18, 3, 0.004, 5.5, and 1.6, respectively, for the studied gas reservoir. Rock-physics-based seismic inversion is then applied to quantify the spatial variation of pore type and permeability. The inversion results indicate that regional stratigraphy has a paramount control on the distribution of pore type and permeability. Moldic pores ([Formula: see text]) are widely distributed near the unconformities, whereas the large intercrystalline porosity and microporosity are distributed below and above the unconformities due to the sea-level regression and transgression, respectively. It is concluded that production problems may occur in porous and permeable intercrystalline-porosity zones and the exploration of residual gas remains in the high porosity yet less permeable moldic-porosity zones.

scholarly journals Reservoir Characterisation of High-Pressure, High-Temperature Zone of Malay Basin Using Seismic Inversion and Artificial Neural Network Approach

2021 ◽  
Vol 11 (21) ◽  
pp. 10248
Author(s):  
Gulbahar Yazmyradova ◽  
Nik Nur Anis Amalina Nik Mohd Hassan ◽  
Nur Farhana Salleh ◽  
Maman Hermana ◽  
Hassan Soleimani
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
High Pressure ◽  
High Temperature ◽  
Quality Factor ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Reservoir Rock ◽  
High Pressure High Temperature ◽  
Artificial Neural

The growing demand for hydrocarbons has driven the exploration of riskier prospects in depths, pressures, and temperatures. Substantial volumes of hydrocarbons lie within deep formations, classified as high pressure, high temperature (HPHT) zone. This study aims to delineate hydrocarbon potential in the HPHT zone of the Malay Basin through the integrated application of rock physics analysis, pre-stack seismic inversion, and artificial neural network (ANN). The zones of interest lie within Sepat Field, located offshore Peninsular Malaysia, focusing on the HPHT area in Group H. The rock physics technique involves the cross-plotting of rock properties, which helps to differentiate the lithology of sand and shale and discriminates the fluid into water and hydrocarbon. The P-impedance, S-impedance, Vp/Vs ratio, density, scaled inverse quality factor of P (SQp), and scaled inverse quality factor of S (SQs) volumes are generated from pre-stack seismic inversion of 3D seismic data. The obtained volumes demonstrate spatial variations of values within the zone of interest, indicating hydrocarbon accumulation. Furthermore, the ANN model is successfully trained, tested, and validated using 3D elastic properties as input, to predict porosity volume. Finally, the trained neural network is applied to the entire reservoir volume to attain a 3D porosity model. The results reveal that rock physics study, pre-stack seismic inversion, and ANN approach helps to recognize reservoir rock and fluids in the HPHT zone.

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