Intelligent classification of carbonate reservoir quality using multisource geophysical logging and seismic data

2022 ◽  
pp. 1-1
Author(s):  
Yang Bai ◽  
Maojin Tan ◽  
Huilan Cao ◽  
Jinliang Tang ◽  
Zhiqiang Liang
Keyword(s):  
Seismic Data ◽  
Carbonate Reservoir ◽  
Reservoir Quality ◽  
Geophysical Logging

Novel geometric classification of 3D seismic and its application to the Habshan clinoforms of Western Oman

2021 ◽  
Vol 40 (3) ◽  
pp. 186-192
Author(s):  
Thomas Krayenbuehl ◽  
Nadeem Balushi ◽  
Stephane Gesbert
Keyword(s):  
Sequence Stratigraphy ◽  
Seismic Data ◽  
Northwestern Part ◽  
Full Potential ◽  
Seismic Sequence ◽  
3D Seismic ◽  
Level Curve ◽  
Seismic Sequence Stratigraphy

The principles and benefits of seismic sequence stratigraphy have withstood the test of time, but the application of seismic sequence stratigraphy is still carried out mostly manually. Several tool kits have been developed to semiautomatically extract dense stacks of horizons from seismic data, but they stop short of exploiting the full potential of seismo-stratigraphic models. We introduce novel geometric seismic attributes that associate relative geologic age models with seismic geomorphological models. We propose that a relative sea level curve can be derived from the models. The approach is demonstrated on a case study from the Lower Cretaceous Kahmah Group in the northwestern part of Oman where it helps in sweet-spotting and derisking elusive stratigraphic traps.

Geostatistical Inversion in Carbonate Reservoirs to Map Reservoir Quality With High Predictability – A Case Study From Onshore Abu Dhabi

2021 ◽  
Author(s):  
S Al Naqbi ◽  
J Ahmed ◽  
J Vargas Rios ◽  
Y Utami ◽  
A Elila ◽  
...  
Keyword(s):  
High Resolution ◽  
Seismic Data ◽  
Pore Volume ◽  
Rock Physics ◽  
Static Model ◽  
Reservoir Quality ◽  
Reservoir Properties ◽  
Geostatistical Inversion ◽  
High Predictability ◽  
The Impact

Abstract The Thamama group of reservoirs consist of porous carbonates laminated with tight carbonates, with pronounced lateral heterogeneities in porosity, permeability, and reservoir thickness. The main objective of our study was mapping variations and reservoir quality prediction away from well control. As the reservoirs were thin and beyond seismic resolution, it was vital that the facies and porosity be mapped in high resolution, with a high predictability, for successful placement of horizontal wells for future development of the field. We established a unified workflow of geostatistical inversion and rock physics to characterize the reservoirs. Geostatistical inversion was run in static models that were converted from depth to time domain. A robust two-way velocity model was built to map the depth grid and its zones on the time seismic data. This ensured correct placement of the predicted high-resolution elastic attributes in the depth static model. Rock physics modeling and Bayesian classification were used to convert the elastic properties into porosity and lithology (static rock-type (SRT)), which were validated in blind wells and used to rank the multiple realizations. In the geostatistical pre-stack inversion, the elastic property prediction was constrained by the seismic data and controlled by variograms, probability distributions and a guide model. The deterministic inversion was used as a guide or prior model and served as a laterally varying mean. Initially, unconstrained inversion was tested by keeping all wells as blind and the predictions were optimized by updating the input parameters. The stochastic inversion results were also frequency filtered in several frequency bands, to understand the impact of seismic data and variograms on the prediction. Finally, 30 wells were used as input, to generate 80 realizations of P-impedance, S-impedance, Vp/Vs, and density. After converting back to depth, 30 additional blind wells were used to validate the predicted porosity, with a high correlation of more than 0.8. The realizations were ranked based on the porosity predictability in blind wells combined with the pore volume histograms. Realizations with high predictability and close to the P10, P50 and P90 cases (of pore volume) were selected for further use. Based on the rock physics analysis, the predicted lithology classes were associated with the geological rock-types (SRT) for incorporation in the static model. The study presents an innovative approach to successfully integrate geostatistical inversion and rock physics with static modeling. This workflow will generate seismically constrained high-resolution reservoir properties for thin reservoirs, such as porosity and lithology, which are seamlessly mapped in the depth domain for optimized development of the field. It will also account for the uncertainties in the reservoir model through the generation of multiple equiprobable realizations or scenarios.

Using Logging and Seismic Data to Identify Boundaries Between Different OWC Units in a Deepwater Carbonate Reservoirs

2021 ◽  
Author(s):  
Kangxu Ren ◽  
Junfeng Zhao ◽  
Jian Zhao ◽  
Xilong Sun
Keyword(s):  
Seismic Data ◽  
Carbonate Rocks ◽  
Carbonate Reservoir ◽  
Carbonate Reservoirs ◽  
Sedimentary Process ◽  
Core Samples ◽  
Tight Carbonate ◽  
Porosity And Permeability ◽  
Seismic Sections ◽  
Parameters Inversion

Abstract At least three very different oil-water contacts (OWC) encountered in the deepwater, huge anticline, pre-salt carbonate reservoirs of X oilfield, Santos Basin, Brazil. The boundaries identification between different OWC units was very important to help calculating the reserves in place, which was the core factor for the development campaign. Based on analysis of wells pressure interference testing data, and interpretation of tight intervals in boreholes, predicating the pre-salt distribution of igneous rocks, intrusion baked aureoles, the silicification and the high GR carbonate rocks, the viewpoint of boundaries developed between different OWC sub-units in the lower parts of this complex carbonate reservoirs had been better understood. Core samples, logging curves, including conventional logging and other special types such as NMR, UBI and ECS, as well as the multi-parameters inversion seismic data, were adopted to confirm the tight intervals in boreholes and to predicate the possible divided boundaries between wells. In the X oilfield, hundreds of meters pre-salt carbonate reservoir had been confirmed to be laterally connected, i.e., the connected intervals including almost the whole Barra Velha Formation and/or the main parts of the Itapema Formation. However, in the middle and/or the lower sections of pre-salt target layers, the situation changed because there developed many complicated tight bodies, which were formed by intrusive diabase dykes and/or sills and the tight carbonate rocks. Many pre-salt inner-layers diabases in X oilfield had very low porosity and permeability. The tight carbonate rocks mostly developed either during early sedimentary process or by latter intrusion metamorphism and/or silicification. Tight bodies were firstly identified in drilled wells with the help of core samples and logging curves. Then, the continuous boundary were discerned on inversion seismic sections marked by wells. This paper showed the idea of coupling the different OWC units in a deepwater pre-salt carbonate play with complicated tight bodies. With the marking of wells, spatial distributions of tight layers were successfully discerned and predicated on inversion seismic sections.

Unsupervised machine learning using 3D seismic data applied to reservoir evaluation and rock type identification

2021 ◽  
pp. 1-69
Author(s):  
Marwa Hussein ◽  
Robert R. Stewart ◽  
Deborah Sacrey ◽  
Jonny Wu ◽  
Rajas Athale
Keyword(s):  
Machine Learning ◽  
Seismic Data ◽  
Rock Type ◽  
Seismic Attributes ◽  
Reservoir Quality ◽  
3D Seismic ◽  
Reservoir Properties ◽  
Unsupervised Machine Learning ◽  
Amplitude Data ◽  
3D Seismic Data

Net reservoir discrimination and rock type identification play vital roles in determining reservoir quality, distribution, and identification of stratigraphic baffles for optimizing drilling plans and economic petroleum recovery. Although it is challenging to discriminate small changes in reservoir properties or identify thin stratigraphic barriers below seismic resolution from conventional seismic amplitude data, we have found that seismic attributes aid in defining the reservoir architecture, properties, and stratigraphic baffles. However, analyzing numerous individual attributes is a time-consuming process and may have limitations for revealing small petrophysical changes within a reservoir. Using the Maui 3D seismic data acquired in offshore Taranaki Basin, New Zealand, we generate typical instantaneous and spectral decomposition seismic attributes that are sensitive to lithologic variations and changes in reservoir properties. Using the most common petrophysical and rock typing classification methods, the rock quality and heterogeneity of the C1 Sand reservoir are studied for four wells located within the 3D seismic volume. We find that integrating the geologic content of a combination of eight spectral instantaneous attribute volumes using an unsupervised machine-learning algorithm (self-organizing maps [SOMs]) results in a classification volume that can highlight reservoir distribution and identify stratigraphic baffles by correlating the SOM clusters with discrete net reservoir and flow-unit logs. We find that SOM classification of natural clusters of multiattribute samples in the attribute space is sensitive to subtle changes within the reservoir’s petrophysical properties. We find that SOM clusters appear to be more sensitive to porosity variations compared with lithologic changes within the reservoir. Thus, this method helps us to understand reservoir quality and heterogeneity in addition to illuminating thin reservoirs and stratigraphic baffles.

Unravel Carbonate Reservoir Heterogeneities Through 3D Seismic Neural Network Application: A Machine Learning Based Approach and Workflow

2021 ◽  
Author(s):  
Dimmas Ramadhan ◽  
Krishna Pratama Laya ◽  
Ricko Rizkiaputra ◽  
Esterlinda Sinlae ◽  
Ari Subekti ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Seismic Data ◽  
Carbonate Reservoir ◽  
3D Seismic ◽  
Carbonate Facies ◽  
Full Field ◽  
Facies Classification ◽  
Unsupervised Neural Network ◽  
Tight Gas Reservoir

Abstract The availability of 3D seismic data undoubtedly plays an important role in reservoir characterization. Currently seismic technology continues to advance at a rapid pace not only in the acquisition but also in processing and interpretation domain. The advance on this is well supported by the digitalization era which urges everything to run reliably fast, effective and efficient. Thanks to continuous development of IT peripherals we now have luxury to process and handle big data through the application of machine learning. Some debates on the effectiveness and threats that this process may automating certain task and later will decrease human workforce are still going on in many forums but still like it or not this machine learning is already embraced in almost every aspect of our life including in oil & gas industry. Carbonate reservoir on the other hand has been long known for its uniqueness compared to siliciclastic reservoir. The term heterogeneous properties are quite common for carbonate due to its complex multi-story depositional and diagenetic facies. In this paper, we bring up our case where we try to unravel carbonate heterogeneity from a massive tight gas reservoir through our machine learning application using the workflow of supervised and unsupervised neural network. In this study, we incorporate 3D PSTM seismic data and its stratigraphic interpretation coupled with the core study result, BHI (borehole image) log interpretation, and our regional understanding of the area to develop a meaningful carbonate facies model through seismic neural network exercises. As the result, we successfully derive geological consistent carbonate facies classification and distribution honoring all the supporting data above though the limitation of well penetration in the area. This result then proved to be beneficial to build integrated 3D geomodel which later can explain the issue on different gas compositions happens in the area. The result on unsupervised neural network also able to serves as a quick look for further sweetspot analysis to support full-field development.

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