3D Seismic attribute optimization technology and application for dissolution caved carbonate reservoir prediction

2011 ◽  
Author(s):  
Lifeng Liu ◽  
Sam Zandong Sun ◽  
Haiyang Wang ◽  
Haijun Yang ◽  
Jianfa Han ◽  
...  
Keyword(s):  
Carbonate Reservoir ◽  
3D Seismic ◽  
Reservoir Prediction ◽  
Seismic Attribute

ANALISA POTENSI MINYAK DAN GAS BUMI DENGAN ATRIBUT SEISMIK PADA BATUAN KARBONAT LAPANGAN *ZEFARA* CEKUNGAN SUMATRA SELATAN

KURVATEK ◽  
2017 ◽  
Vol 1 (2) ◽  
pp. 21-31
Author(s):  
Fatimah Miharno
Keyword(s):  
Instantaneous Frequency ◽  
Carbonate Reservoir ◽  
Control Analysis ◽  
High Porosity ◽  
Hydrocarbon Accumulation ◽  
Seismic Attribute ◽  
Data Set ◽  
Log Data ◽  
Low Amplitude ◽  
Rms Amplitude

ABSTRACT*Zefara* Field formation Baturaja on South Sumatra Basin is a reservoir carbonate and prospective gas. Data used in this research were 3D seismik data, well logs, and geological information. According to geological report known that hidrocarbon traps in research area were limestone lithological layer as stratigraphical trap and faulted anticline as structural trap. The study restricted in effort to make a hydrocarbon accumulation and a potential carbonate reservoir area maps with seismic attribute. All of the data used in this study are 3D seismic data set, well-log data and check-shot data. The result of the analysis are compared to the result derived from log data calculation as a control analysis. Hydrocarbon prospect area generated from seismic attribute and are divided into three compartments. The seismic attribute analysis using RMS amplitude method and instantaneous frequency is very effective to determine hydrocarbon accumulation in *Zefara* field, because low amplitude from Baturaja reservoir. Low amplitude hints low AI, determined high porosity and high hydrocarbon contact (HC).  Keyword: Baturaja Formation, RMS amplitude seismic attribute, instantaneous frequency seismic attribute

Research and application of seismic porosity inversion method for carbonate reservoir based on Gassmann’s equation

2021 ◽  
Vol 13 (1) ◽  
pp. 122-129
Author(s):  
Kaiyuan Liu ◽  
Li Qin ◽  
Xi Zhang ◽  
Liting Liu ◽  
Furong Wu ◽  
...  
Keyword(s):  
High Accuracy ◽  
Field Application ◽  
Carbonate Reservoir ◽  
Carbonate Reservoirs ◽  
Pore Geometry ◽  
Inversion Method ◽  
Well Testing ◽  
Seismic Attribute ◽  
Gassmann’S Equation ◽  
Inclusion Theory

Abstract Carbonate rocks frequently exhibit less predictable seismic attribute–porosity relationships because of complex and heterogeneous pore geometry. Pore geometry plays an important role in carbonate reservoir interpretation, as it influences acoustic and elastic characters. So in porosity prediction of carbonate reservoirs, pore geometry should be considered as a factor. Thus, based on Gassmann’s equation and Eshelby–Walsh ellipsoidal inclusion theory, we introduced a parameter C to stand by pore geometry and then deduced a porosity calculating expression from compressional expression of Gassmann’s equation. In this article, we present a porosity working flow as well as calculate methods of every parameter needed in the porosity inverting equation. From well testing and field application, it proves that the high-accuracy method is suitable for carbonate reservoirs.

Seismic attribute for characterization and prediction of carbonate faulted karst reservoirs in Tarim Basin, China

2021 ◽  
pp. 1-36
Author(s):  
Zhiwei Xiao ◽  
Li Wang ◽  
Ruizhao Yang ◽  
Dewei Li ◽  
Lingbin Meng
Keyword(s):  
Tarim Basin ◽  
Oil And Gas ◽  
Prediction Method ◽  
Strike Slip ◽  
Fault System ◽  
Bright Spot ◽  
Upper Ordovician ◽  
Reservoir Prediction ◽  
Seismic Attribute ◽  
Gas Field

An ultradeep, faulted karst reservoir of Ordovician carbonate was discovered in the Shunbei area of the Tarim Basin. Fractured-cavity reservoirs buried beneath the large thickness of upper Ordovician mudstone were formed along the fault-karst belts. The hydrocarbon accumulation in these reservoirs is controlled by the fault system, and the oil-gas accumulation was affected by karstification and hydrothermal reformation. Previous studies and 2D modeling revealed that the reservoirs had “bright spot” amplitude responses like “string beads,” and they have developed along the strike-slip faults. However, describing such a complex fault-controlled karst system is still a difficult problem that has not been well addressed. We have sought to instruct the attribute expression of faulted karst reservoirs in the northern part of the Tarim Basin. We applied coherence and fault likelihood (FL) seismic attributes to image faults and fractures zones. We then used a trend analysis method to calculate the residual impedance from the impedance of the acoustic inversion, using the fact that residual impedance has higher lateral resolution in reservoir predictions. Finally, we integrated the coherence, FL, and residual impedance attributes into a new seismic attribute, the “fault-vuggy body,” with a certain fusion coefficient. The fault-vuggy body attribute establishes a connection between faults and karst cavities. This method could help in the characterization and prediction of carbonate faulted karst reservoirs. Available drilling data were used to validate that the fused fault-vuggy body attribute was an effective reservoir prediction method. As the seismic sections and slices along the layer help delineate, the distribution of bright spots and strike-slip faults indicates that the main strike-slip fault zones are the most favorable reservoirs in the Shunbei Oil and Gas Field.

scholarly journals Characterization of reservoir sands using 3D seismic attributes in the coastal swamp area of Niger Delta Basin

2021 ◽  
Author(s):  
Oluwatoyin Khadijat Olaleye ◽  
Pius Adekunle Enikanselu ◽  
Michael Ayuk Ayuk
Keyword(s):  
Seismic Data ◽  
Niger Delta ◽  
Seismic Attributes ◽  
Effective Porosity ◽  
High Porosity ◽  
Hydrocarbon Accumulation ◽  
3D Seismic ◽  
Seismic Attribute ◽  
Niger Delta Basin

AbstractHydrocarbon accumulation and production within the Niger Delta Basin are controlled by varieties of geologic features guided by the depositional environment and tectonic history across the basin. In this study, multiple seismic attribute transforms were applied to three-dimensional (3D) seismic data obtained from “Reigh” Field, Onshore Niger Delta to delineate and characterize geologic features capable of harboring hydrocarbon and identifying hydrocarbon productivity areas within the field. Two (2) sand units were delineated from borehole log data and their corresponding horizons were mapped on seismic data, using appropriate check-shot data of the boreholes. Petrophysical summary of the sand units revealed that the area is characterized by high sand/shale ratio, effective porosity ranged from 16 to 36% and hydrocarbon saturation between 72 and 92%. By extracting attribute maps of coherence, instantaneous frequency, instantaneous amplitude and RMS amplitude, characterization of the sand units in terms of reservoir geomorphological features, facies distribution and hydrocarbon potential was achieved. Seismic attribute results revealed (1) characteristic patterns of varying frequency and amplitude areas, (2) major control of hydrocarbon accumulation being structural, in terms of fault, (3) prospective stratigraphic pinch-out, lenticular thick hydrocarbon sand, mounded sand deposit and barrier bar deposit. Seismic Attributes analysis together with seismic structural interpretation revealed prospective structurally high zones with high sand percentage, moderate thickness and high porosity anomaly at the center of the field. The integration of different seismic attribute transforms and results from the study has improved our understanding of mapped sand units and enhanced the delineation of drillable locations which are not recognized on conventional seismic interpretations.

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.

3D Multi-Level Facies Constrained Geostatistical Inversion for Predicting Carbonate Thin Reservoirs

2021 ◽  
Author(s):  
Tongcui Guo ◽  
Lirong Dou ◽  
Guihai Wang ◽  
Dongbo He ◽  
Hongjun Wang ◽  
...  
Keyword(s):  
Prediction Method ◽  
Rock Physics ◽  
Simulation Method ◽  
Horizontal Resolution ◽  
Carbonate Reservoir ◽  
Carbonate Reservoirs ◽  
Reservoir Prediction ◽  
Geostatistical Inversion ◽  
Water Layers ◽  
Multi Level

Abstract Carbonate reservoirs are highly heterogeneous and poor in interwell connectivity. Therefore, it is difficult to predict the thin oil layers and water layers inside the carbonate reservoir with thickness less than 10 ft by seismic data. Based on the petrophysical analysis with core and well logging data, the carbonate target layers can be divided into two first level lithofacies (reservoir and non-reservoir) and three second-level lithofacies (oil, water and non-reservoir) by fluids. In this study, the 3D lithofacies probabilistic cubes of the first level and second-level level lithofacies were constructed by using the simulation method of well-seismic cooperative waveform indication. Afterwards, constrained by these probability cubes, the prestack geostatistical inversion was carried out to predict the spatial distribution of thin oil layers and water layers inside the thin reservoir. The major steps include: (1) Conduct rock physics analysis and lithofacies classification on carbonate reservoirs; (2) Construct the models constrained by two-level lithofacies; (3) Predict thin reservoirs in carbonates by prestack geostatistical inversion under the constraint of two-level lithofacies probability cubes. The prediction results show that through the two-level lithofacies-controlled prestack geostatistical inversion, the vertical and horizontal resolution of thin oil layers and water layers in carbonate reservoirs has been improved significantly, and the accuracy of thin oil reservoir prediction and the analyzing results of interwell oil layer connectivity have been improved significantly. Compared with the actual drilling results, the prediction results by 3D multi-level lithofacies-controlled inversion are consistent with the drilling results, and the details of thin carbonate reservoirs can be predicted. It has been proved that this method is reasonable and feasible. With this method, the prediction accuracy on thin reservoirs can be improved greatly. Compared with the conventional geostatistical inversion results, the 3D multi-level lithofacies-controlled inversion can improve significantly the vertical and horizontal resolution of prediction results of thin reservoirs and thin oil layers, and improve the reliability of interwell prediction results. For the prediction of thin carbonate reservoirs with serious heterogeneity, the 3D multi-level lithofacies-controlled inversion is an effective prediction method.

Sign in / Sign up

Export Citation Format

Share Document