A New Reservoir Prediction Method Based on Geological Seismic Conditioning for Complex Barrier Island and Its Application at H Oil Field

2019 ◽  
Author(s):  
Xin Chen ◽  
Suhong Zhang ◽  
Jin Ou ◽  
Yufeng Ye ◽  
Lei Xu ◽  
...  
Keyword(s):  
Prediction Method ◽  
Barrier Island ◽  
Oil Field ◽  
Reservoir Prediction ◽  
New Reservoir

A new reservoir prediction method: PCA value‐weighted attribute optimization

2011 ◽  
Author(s):  
Lifeng Liu ◽  
Sam Zandong Sun ◽  
Haiyang Wang ◽  
Haijun Yang ◽  
Jianfa Han ◽  
...  
Keyword(s):  
Prediction Method ◽  
Reservoir Prediction ◽  
New Reservoir

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.

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.

Reservoir Prediction Under Control of Sedimentary Facies

2017 ◽  
Vol 25 (03) ◽  
pp. 1750022
Author(s):  
Xiuwei Yang ◽  
Peimin Zhu
Keyword(s):  
Seismic Data ◽  
Low Frequency ◽  
Prediction Method ◽  
Rock Physics ◽  
Real Data ◽  
Sedimentary Facies ◽  
Seismic Inversion ◽  
Rock Properties ◽  
Reservoir Prediction ◽  
Frequency Model

Acoustic impedance (AI) from seismic inversion can indicate rock properties and can be used, when combined with rock physics, to predict reservoir parameters, such as porosity. Solutions to seismic inversion problem are almost nonunique due to the limited bandwidth of seismic data. Additional constraints from well log data and geology are needed to arrive at a reasonable solution. In this paper, sedimentary facies is used to reduce the uncertainty in inversion and rock physics modeling; the results not only agree with seismic data, but also conform to geology. A reservoir prediction method, which incorporates seismic data, well logs, rock physics and sedimentary facies, is proposed. AI was first derived by constrained sparse spike inversion (CSSI) using a sedimentary facies dependent low-frequency model, and then was transformed to reservoir parameters by sequential simulation, statistical rock physics and [Formula: see text]-model. Two numerical experiments using synthetic model and real data indicated that the sedimentary facies information may help to obtain a more reasonable prediction.

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