Carbonate Brazilian pre-salt fields have a large number of faults detected by seismic and well data. Nevertheless, because of limitations in seismic resolution, all existent faults cannot be identified. That is one of the main challenges for understanding related heterogeneities (vugs, karst) and the flow behavior. This paper deals with a fault analysis and modeling using an original approach and fault data of three pre-salt reservoirs.
One possible approach for characterizing and modeling the fault network (
The results presented on this article lead us to discuss the importance of how to choose the samples for modeling sub-seismic faults based on the ensemble of seismic faults available. This article answers the question about which available seismic faults we should use for estimating fractal dimension, should we use all available seismic faults near of the reservoir area or use only the faults inside the reservoir contour. After this short discussion on the fractal dimension choice from a spatial distribution point of view, the impact of this choice on flow was illustrated. The sub-seismic fault models were modeled using different fractal dimension. Subsequently, an upscaling step using analytical upscaling (
Characterizing sub-seismic faults has a major impact on the overall flow behavior of the field. The chosen methodology has been applied only on synthetic cases but never published using real data. This work will interest a practicing engineer. The fault network of these neighbor reservoirs allows us to illustrate the importance on the choice of fractal dimension for characterizing the fault network and its impact on the subseismic models and fluid displacement, consequently on production.
Li, Bingjian (Schlumberger Oil Field Services) | Chen, Yong-Hua (Southwestern Energy, Woodland, USA) | Gawankar, Kiran (Schlumberger-Doll Research) | Miller, Camron K. (Schlumberger Oil Field Services) | Xu, Weixin (Schlumberger Oil Field Services) | Laronga, Rob J. (Schlumberger Oil Field Services) | Omeragic, Dzevat (Southwestern Energy, Woodland, USA)
Distinguishing open natural fractures from healed fractures has been a significant challenge in shale formations drilled with oil-based mud. Ultrasonic imaging tools can locate open fractures, but such data is seldom acquired due to concerns related to the effects of heavy mud and, in high-angle wells, operational efficiency and tool eccentralization. Until now, the microelectrical image tools in the market were not capable of differentiating open fractures from healed fractures in oil-based mud.
A new, high-definition oil-based mud microelectrical imager has been deployed that operates at high frequencies and provides images with high borehole coverage. This new tool can identify natural fractures, sub-seismic faults, and other geological features in the reservoirs. In addition to high-resolution images of formation resistivity, an advanced inversion processing can be applied to generate resolution-matched images of the quantified standoff between each sensor in the array and the borehole wall. Such standoff images are of special value for differentiating open fractures from healed fractures. The use of these standoff images are presented in several recent case studies from U.S. shale plays. In the first case study from a pilot shale well in the northeast, natural fractures are identified on the new microelectrical imager and then further interpreted as open, partially open or healed fractures based on the inverted standoff images. Such open fracture interpretation has been validated by ultrasonic image data from the same well. In the second case study from an Eagle Ford Shale lateral in south Texas, both natural fractures and sub-seismic faults were detected. Interestingly, one of the interpreted open faults based on standoff images was even evident on dynamic pressure data in a monitoring well nearby during the stimulation process.
Natural fractures can impact the shale reservoir quality, completion quality, or both, depending on the fracture types and intensity. Therefore, it is beneficial to have a reliable dataset to sort fractures by their type: open, partially open and healed.
Uncertainties associated with fracture properties are usually large, and significantly impact the reservoir model flow behaviour. The analysis of these uncertainties is therefore a necessary task for performing more reliable production forecasts and optimization via fractured reservoir flow models. However this task may quickly become intractable considering the overwhelming uncertain fracture properties ranges to investigate. A workflow is presented that allows one to embrace large multi-scale fracture uncertainties and to analyze their impact on geologically-consistent reservoir models in term of reservoir-related connectivity properties. Fractured reservoir model classification is then performed based on the ranges of connectivity values that result from the fracture properties uncertainties, thus facilitating the analysis of the effect of fracture uncertainties on the reservoir flow models.
A stochastic fractal fault model has been used to investigate the effects of fault uncertainties at seismic and sub-seismic scales on a reservoir model. Uncertainty analyses have been performed for the following parameters: (1) the fractal dimension that controls the fault spatial distribution; (2) the fault length distribution defined from a power-law statistical distribution. The sub-seismic fault orientation is constrained by the seismic fault network. The fault network orientation is assumed to have a NW-SE trend. The reservoir model is a synthetic, but geologically-realistic, 12 km by 15 km reservoir with three facies and seven layers. A five-spot well configuration involving four injectors and one producer is considered. The injector-producer connectivity is evaluated via a single-source shortest path graph algorithm that computes the injector-producer distances via the reservoir cell transmissivities and volumes. The effects of the fault uncertainties on the injector-producer connectivity are estimated from a large representative sets of fault network realizations.
The different steps of the workflow are rather fast: the fault network generation takes a few seconds, the conversion to an equivalent reservoir flow model takes a few minutes, and the graph connectivity analysis only a few seconds. This low processing time allows one to analyze a large number of fault network realizations, thus better estimating the impact of the large fracture uncertainties on the reservoir model. The distribution of the injector-producer connectivity properties is rather multi-modal and dispersed, however relevant classes of fractured reservoir models could be identified accordingly to the fault fractal dimension. These different classes can be used to estimate fractured reservoir model occurrences based on specific reservoir connectivity properties, thus allowing one to identify the most probable flow reservoir model configurations considering large fracture properties uncertainties.
The proposed workflow can be used to analyze the effects of the multi-scale fracture uncertainties on equivalent fractured reservoir flow models. Thus facilitating the classification and identification of the most relevant flow models, used subsequently for production forecasts and optimization.
Fractured reservoirs are more complicated than matrix reservoirs and they do require to be evaluated correctly. Information on the accurate characterization of fault zones and on the way in which faults and fractures affect fluid flow are needed. In this study, a 3D multi component and multi directional deterministic operator is designed and developed to detect sub-seismic faults from seismic data that converge at a point, to map conduits in naturally fractured reservoirs. This operator is cubic and composed of nine symmetrical two dimensional plane templates. They are designed to search all possible directions and angles, to detect and match any two sub-seismic faults that meet at its center. It covers seventy two detection directions, and angles range from 22.5° to 157.5°. Three binary decisions are performed with each of the nine symmetrical planes. the first is for detecting a sub seismic fault at a point, the second is for evaluating a linear arrangement of these points and the third is to verify if two linear arrangements exist on the same plane.
The case study is presenting conduits maps to demonstrate the behavior of three different sub-seismic fault size ranges. The conduits intensities vary within and for each formation. They are minimal to absent in reservoirs top seal. This technique detects only small scaled faults. It can't detect primary sedimentary structures and random noises. It provides an output related to the variation of dual porosity and permeability and can be considered a supporting input for more realistic reservoir simulation. This technique helps determine the effect of natural fractures in the reservoirs as early as possible so that the evaluations and planning can be done correctly from day one. It is a reliable tool for reservoir top seal fracturing evaluation, sweet spots in fractured reservoirs delineation and potential compartments identification.
A predictive knowledge of fault zone structure and transmissibility can have an enormous impact on the economic viability of exploration targets and generate considerable benefits during reservoir management. Understanding the effects of faults and fractures on fluid flow behavior and distribution within hydrocarbon provinces has therefore become a priority. To model fluid flow in hydrocarbon reservoirs, it is essential to gain a detailed insight into the evolution, structure and properties of faults and fractures. Fault zones can have highly complex geometries, with strain being accommodated not just on a single fault plane but within a complex array of faults known as a damage zone.
An integrated optimization workflow was developed to characterize seismic and sub-seismic fault networks from history-matching. A fractal model of fault networks is optimized via the gradual deformation of stochastic realizations of fault density maps, fault spatial and length distributions. In order to facilitate the history-matching, connectivity analysis tools were developed for characterizing wells-reservoir and well-to-well connectivity. Indeed these connectivity properties usually depend on the fault network realization and may be strongly correlated with the reservoir flow dynamics. Connectivity analyses were performed on a fractured reservoir model involving a five-spot well configuration with four injectors and one producer. The connectivity was estimated from shortest path algorithms applied on a graph representation of the reservoir model. Several reservoir simulations were performed for different fault network realizations to seek correlations between injector-producer connectivity and water breakthrough time. The impact of the fracture properties uncertainties on the wells-reservoir connectivity was estimated via the cumulated connected volume computed for each well. This connectivity measure provides a mean to characterize and classify fault network realizations. Correlations were found between the water breakthrough time and the injector-producer connectivity, thus allowing one to identify the most probable fault network realizations to match the observed water breakthrough time. Finally, for a given fault network realization, it is shown how the oil recovery can be optimized by correlating injectors rates with the injector-producer connectivity. A gain of 3.106 m3 in produced oil was obtained, while retarding the water breakthrough time by 16 years, compared with a case where all injectors have the same rate. The proposed methodology and tools facilitate the history-matching of fractured reservoir, providing consistent reservoir models that can be used for production forecast and optimization.
The use of uncertainty analysis as a tool in reservoir studies is becoming more and more common inside Petrobras and all around the world. However, in fields with production history, traditional uncertainty analysis, combining possible values of uncertainty variables, can lead to models that poorly represent the reservoir and to results that do not respect the available dynamic data. During uncertainty analysis process, history matching evaluation can considerably reduce the existing uncertainties.
The methodology used in this work is based on experimental design and response surfaces. Besides the cumulative production response surface, another one is generated to represent the quality of the history matching. Only cases with a good history matching are selected as input to the Montecarlo simulation. With this technique, it is possible to evaluate the initially defined probability distributions and, if necessary, to redefine shape or limits for the probability density curve.
The methodology was applied in a real study in Petrobras. There are uncertainties related to faults, absolute permeability and also related to the existing fluid properties. Although there are other wells in the same block, the studied area is located in a sea-bottom slope region, where water depth varies considerably within the block, possibly influencing the oil quality.
Since there are two wells operating in the studied region, one producer and one injector, the developed analysis took the existing dynamic data into account, reducing model uncertainties.
Simulating fractured carbonate oil reservoirs using the conventional single-porosity/single-permeability (SPSP) model does not normally capture the whole fluid flow mechanism in the system. A dual-porosity/dual-permeability model (DPDP) conditioned by field dynamic data can lead to better fluid saturation matches.
This paper presents a new approach to history matching of naturally fractured reservoirs using a DPDP model that incorporates integrating production field performance data into the geological fracture model. A case example from a carbonate reservoir model will be used to illustrate this approach.
The construction of the fracture model was done by emplying structural lineaments from 3D seismic data, and then enhanced by integrating engineering data into the model. The engineering data included mud losses during drilling, flowmeter surveys, transient well test analysis and water production data.
The upscaled 3D fracture model was then used for history matching. With only minor modifications to the fracture model, a simulation model yielded a reliable match for water production. Furthermore, eighty percent of mud loss occurrences were in agreement with the 3D seismic lineaments mapping.
The area under study is part of a carbonate reservoir that has been on stream for the last 30 years. The area of interest has been produced at a limited rate under fluid expansion augmented by a weak aquifer drive before a peripheral waterflooding project was put in place.
Characterization work was made to obtain an improved model of the three main heterogeneities: matrix, stratiform Super-K intervals and fractures. The natural fractures were modeled using a few image logs run on vertical wells and from curvature analysis made on the top structure seismic map. Consentino et al. 1 described the modeling and simulation work of a sector model located in the northern part of the studied reservoir. The authors showed how dual media simulation model was used to match the localized water breakthrough experienced in few up-dip wells. Since then, numerous horizontal wells were drilled. Additional data, both static and dynamic were gathered from these wells and were used to improve the fracture model.
Li, Bingjian (Schlumberger) | Al-Awadi, Mishari Ameen (Kuwait Oil Company) | Perrin, Christian (Schlumberger) | Al-Khabbaz, Mohammed (Schlumberger) | Al-Ashwak, Samar (Kuwait Oil Company) | Al-Qadeeri, Bashar (Kuwait Oil Company)
Evaluating natural fractures in tight carbonate reservoirs during the exploration and early development stages is critical in order to reduce geological uncertainty and determine well trajectory in future horizontal drilling. Challenges are often found in both acquiring the adequate data and assessment of the fractures/sub-seismic faults in the oil based mud borehole environment.
This paper summarizes part of the experience learned from the use of an optimal dataset in addition to a workflow on fracture characterization for tight deep carbonate reservoirs in Kuwait. In the process of exploration and development of these particular reservoirs, oil-based mud has been used in the drilling process due to the concerns of wellbore stability. Acoustic images and core was acquired in the early stages of the field development. After the invention of a micro-resistivity imaging tool it was used in combination with the acoustic imaging for integrated and enhanced formation evaluation, which allowed reduced coring for a cost saving. The paper explains the advantages and limitations of each image dataset and describes how the acoustic and micro-resistivity images are complementary to each other. The paper also presents how different datasets gives partial contribution to the overall geological understanding of the field. More importantly, the case study shows that the combination of both image data sets provides a much better and more complete picture of fractures in the wellbore with limited core calibration. Smaller scale faults, which are usually not detected or poorly imaged on seismic, can be interpreted on images with definition of vertical displacement through the integration of well correlation and cross sections. The output from this study provides an essential database for well completion decisions, fracture reservoir modeling, infill drilling plan and future horizontal well placement.