A Multi-Point Geostatistical Seismic Inversion Method Based on Local Probability Updating of Lithofacies

In order to solve the problem that elastic parameter constraints are not taken into account in local lithofacies updating in multi-point geostatistical inversion, a new multi-point geostatistical inversion method with local facies updating under seismic elastic constraints is proposed. The main improvement of the method is that the probability of multi-point facies modeling is combined with the facies probability reflected by the optimal elastic parameters retained from the previous inversion to predict and update the current lithofacies model. Constrained by the current lithofacies model, the elastic parameters were obtained via direct sampling based on the statistical relationship between the lithofacies and the elastic parameters. Forward simulation records were generated via convolution and were compared with the actual seismic records to obtain the optimal lithofacies and elastic parameters. The inversion method adopts the internal and external double cycle iteration mechanism, and the internal cycle updates and inverts the local lithofacies. The outer cycle determines whether the correlation between the entire seismic record and the actual seismic record meets the given conditions, and the cycle iterates until the given conditions are met in order to achieve seismic inversion prediction. The theoretical model of the Stanford Center for Reservoir Forecasting and the practical model of the Xinchang gas field in western China were used to test the new method. The results show that the correlation between the synthetic seismic records and the actual seismic records is the best, and the lithofacies matching degree of the inversion is the highest. The results of the conventional multi-point geostatistical inversion are the next best, and the results of the two-point geostatistical inversion are the worst. The results show that the reservoir parameters obtained using the local probability updating of lithofacies method are closer to the actual reservoir parameters. This method is worth popularizing in practical exploration and development.

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

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.

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

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.

Study of Geostatistical Inversion in the Lithologic Distribution and Velocity Modeling of Thick Igneous Rock in the FY Area, Northern Tarim Basin, China

In the northern Tarim Basin, a large number of thick igneous rocks are encountered in the drilling process in the Permian. Their lithology and velocity are very strongly, which has a great influence on migration imaging of the “beaded” areas. It is very important to conduct the fine lithology identification and high-precision velocity modeling of the igneous rocks for the exploration and development of the reservoirs. A geostatistical inversion method to obtain the igneous-rock lithologic distribution pattern and velocity modeling in the FY area of the northern Tarim Basin is introduced in this paper. The results show that the application of the geostatistical inversion method greatly improves the resolution of lithology identification. This helps us further understand the Permian igneous rocks distribution in the FY area. Comparison between the seismic facies classification maps of the FY study area shows that the obtained velocity model can reflect the lateral distribution of igneous rocks well. At the same time, the velocity model can reflect the variation of igneous rocks velocity in detail and has a high precision. The average velocity error of the wells participating in the inversion is less than 2%, and the minimum average velocity error is 0.23%. Finally, the velocity model is applied to seismic data processing, and the processing results indicate that it can help to improve seismic migration imaging. The study demonstrates that the geostatistical inversion method can provide a high-precision velocity model for formation pressure prediction and seismic data processing and interpretation, ultimately guiding the exploration and development of oil.

The impact of natural fractures on heat extraction from tight Triassic sandstones in the West Netherlands Basin: a case study combining well, seismic and numerical data

The positive impact that natural fractures can have on geothermal heat production from low-permeability reservoirs has become increasingly recognised and proven by subsurface case studies. In this study, we assess the potential impact of natural fractures on heat extraction from the tight Lower Buntsandstein Subgroup targeted by the recently drilled NLW-GT-01 well (West Netherlands Basin (WNB)). We integrate: (1) reservoir property characterisation using petrophysical analysis and geostatistical inversion, (2) image-log and core interpretation, (3) large-scale seismic fault extraction and characterisation, (4) Discrete Fracture Network (DFN) modelling and permeability upscaling, and (5) fluid-flow and temperature modelling. First, the results of the petrophysical analysis and geostatistical inversion indicate that the Volpriehausen has almost no intrinsic porosity or permeability in the rock volume surrounding the NLW-GT-01 well. The Detfurth and Hardegsen sandstones show better reservoir properties. Second, the image-log interpretation shows predominately NW–SE-orientated fractures, which are hydraulically conductive and show log-normal and negative-power-law behaviour for their length and aperture, respectively. Third, the faults extracted from the seismic data have four different orientations: NW–SE, N–S, NE–SW and E–W, with faults in proximity to the NLW-GT-01 having a similar strike to the observed fractures. Fourth, inspection of the reservoir-scale 2D DFNs, upscaled permeability models and fluid-flow/temperature simulations indicates that these potentially open natural fractures significantly enhance the effective permeability and heat production of the normally tight reservoir volume. However, our modelling results also show that when the natural fractures are closed, production values are negligible. Furthermore, because active well tests were not performed prior to the abandonment of the Triassic formations targeted by the NLW-GT-01, no conclusive data exist on whether the observed natural fractures are connected and hydraulically conductive under subsurface conditions. Therefore, based on the presented findings and remaining uncertainties, we propose that measures which can test the potential of fracture-enhanced permeability under subsurface conditions should become standard procedure in projects targeting deep and potentially fractured geothermal reservoirs.