Geo-modelling is usually done to honor static data such as core, well logs and seismic acoustic impedance (AI) map where available. Once the static geo-model is complete, history matching is carried out by tuning the static model properties until the model reproduces observed dynamic behavior. The objective of this paper is to showcase how a systematic a priori integration of dynamic elements into geo-modelling eliminated the need for history matching. These dynamic elements are; connected reservoir regions CRR (
CRRs were defined based on time-lapse shut-in pressure trend groups. Core and log data were grouped on the basis of the identified CRR and used to build CRR-based Neural Network models for predicting permeability logs of non-cored wells within each CRR. The geo-modeler then created two geo-realizations by using the permeability logs within each CRR to distribute permeability within the CRR using two assumptions of variogram lengths (i) variogram range obtained from analysis of limited core data, (ii) variogram range required to ensure intra-CRR connectivity. Pressure transient was simulated for wells with observed PTA data using the two realizations, and a comparison of the log-log plots of simulated pressure transient derivative and observed pressure transient derivative were used to determine the quality of each realization for each well. The realization that provided the least squares of error across all the wells was selected as base-case geo-model. Permeability correction coefficients were applied on the base-case geo-model until PTA kh were acceptably matched. The resulting permeability log at the PTA well is referred to as PTA-corrected permeability log. Some cored wells were originally exempted from the neural-network permeability modelling because they didn't have logs (sonic, density and neutron logs). Hybrid permeability logs were derived from a combination of the predicted permeability logs and core permeability at these well locations.
All permeability correction logs (i) PTA-corrected permeability logs and (ii) Hybrid permeability logs were then fed back into the geo-modeling workflow to generate an improved permeability distribution which respects core data, PTA kh, and CRRs.
The do-nothing simulation run has more than 80% of wells’ pressure data acceptably history matched. This application demonstrates that a priori integration of dynamic elements like CRR, PTA kh, and the use of CCR-based permeability modeling results in a better characterized geo-model with potential for eliminating the need for history matching.
F field is located in the Middle East and was discovered in 1980's. The bulk of the proven liquid hydrocarbons are contained in the B formation (carbonate), which averages some 200m in thickness in the study area. The hydrocarbons are contained in a low relief NW-SE trending anticlinal structure. Deposition of the reservoir sequence occurred during the late Cemnomanian to Early Turonian in the Mesopotamian Basin.
Oil production started few years ago and currently there are more than 40 producing wells in the centre of the structure. The uneven well distribution limits the understanding of 3D reservoir characterisation, explicitly in the flank areas. Only one conventional core with good recovery was available for reservoir B, which makes it somewhat difficult to delineate the internal architecture of the carbonate ramp.
A total of seven lithofacies were identified which are Rudistic Floatstone Wackestone, Bioclastic Wackestone, Bioturbated Bioclastic Packstone (BBP), Rudistic Rudstone Wackestone, Bioclastic Floatstone Wackestone, Heterolithic Bioturbated Bioclastic Wackestone and Bioclastic Mudstone. A conceptual depositional environment with lithofacies association was generated by using core descriptions, regional studies and analogues.
A fit for purpose integrated reservoir characterisation study was carried out in 2014 with main inputs from Nuclear Magnetic Resonance (NMR) and image logs in conjunction with digital rock analysis (DRA), conventional open hole well logs, core laboratory analysis, mud logs, pressure and well test data. Several rock typing approaches were developed; including Flow Zone Indicator (FZI) or Rock Quality Index (RQI), Lucia Rock Class, Clerke Pore Size technique from Mercury Injection Capillary Pressure (MICP), multi-variate cluster analysis (biased and normalized), cluster analysis (non-biased and non-normalized) and self-organizing maps (biased but non-normalized). From these FZI, an optimum of seven Hydraulic Units (HU) were selected where FZI 1 being the best flow unit, and FZI 7 is the poorest.
A relationship between FZI with lithofacies was then established by comparing both on depth plot as well as porosity-permeability cross plot. Different FZI can be seen for one lithofacies, for instance BBP consists of FZI 1 to 4 which indicates the changes in reservoir quality within the same lithofacies. The same relationship was extended towards depofacies, and resulted with lateral segregation of depositional environment according to its reservoir quality.
This exercise confirms the heterogeneity within B formation, and it captures the changes in reservoir quality laterally and vertically. This detailed understanding of the carbonate architecture was translated into a 3D geological model in order to minimize the uncertainties for dynamic simulation and future field development plans.
We present a new method for seismic reservoir characterization and reservoir-property modeling on the basis of an integrated analysis of 3D-seismic data and hydraulic flow units, and apply it to an example of a producing reservoir offshore Western Australia. Our method combines hydraulic-unit analysis with a set of techniques for seismic reservoir characterization including rock physics analysis, Bayesian inference, prestack seismic inversion, and geostatistical simulation of reservoir properties. Hydraulic units are geologic layers and zones characterized by similar properties of fluid flow in porous permeable media, and are defined at well locations on the basis of logs, core measurements, and production data. However, the number of wells available for hydraulic- unit analysis is often extremely limited. In comparison, the lateral coverage and resolution of 3D-seismic data are excellent, and can thus be used to extend hydraulic-unit analysis away from well locations into the full 3D reservoir volume. We develop a probabilistic relationship between optimal 3D-seismic-data attributes and the hydraulic units that we determine at well locations. Because porosity and permeability distributions are estimated for each hydraulic flow unit as part of the process, we can use the 3D seismic probabilistic relationships to constrain geostatistical realizations of porosity and permeability in the reservoir, to be consistent with the flow-unit analysis. Reservoir models jointly constrained by both 3D-seismic data and hydraulic flow-unit analysis have the potential to improve the processes of reservoir characterization, fluid-flow performance forecasting, and production data or 4D-seismic history matching.
Rock typing is an essential reservoir characterization tool to reflect the spatial variation in initial fluid distribution and flow behavior characteristics. Rock typing techniques are generally based on porosity-permeability relationships to establish the types of rocks present in the reservoir. These techniques lack in effective segregation of reservoir into definite number of zones/rock-types with clear boundaries. This results in a non-unique rock-typing scheme with arbitrary number of RRTs depending on data manipulation. Moreover, these RRTs do not correspond to separate J-function curves due to negligence to water saturation term. To overcome these problems, a new approach for reservoir zonation is developed which has been tested in a few off-shore sandstone reservoirs.
This paper illustrates a robust method of rock typing using a new theoretical development and mathematical formulation. The method integrates irreducible water saturation term with modified Carmen-Kozney equation using a proposed pore-throat dependent water saturation function [Swirr = exp (-art)]. The generalized porosity-permeability-saturation equation [K = AØ3(lnSwirr)B] thus derived is fitted on the Routine Core Analysis (RCAL) data for division of reservoir section into different layers having unique coefficient-exponent set (A, B) representing RRTs.
Application of this method resulted in an effective RRT scheme as evident from the unique coefficient-exponent sets as well as separate J-function curves. The unique porosity-permeability-saturation relationship existing for each RRT has been thus obtained from the RCAL data. This relationship can be used as an efficient tool for reservoir zonation. Reservoir zonation algorithm, analysis results and validation procedures are discussed using field examples.
Reservoir characterization is the key to improve reservoir performance prediction and recovery optimization. This paper presents a novel approach to effectively segregate the reservoir into definite number of RRTs using only RCAL data. This work also presents a theoretically derived K-Ø-Swirr relationship based on pore scale attributes. Irreducible water saturation, being an eminent parameter describing the internal architecture of the rock, is included in formulation and derivation of a theoretical framework to address classification of RRTs.
We present a new method for seismic reservoir characterization and reservoir property modeling based on an integrated analysis of 3D seismic data and hydraulic flow units, and apply it to an example of a producing reservoir offshore Western Australia. Our method combines hydraulic unit analysis with a set of techniques for seismic reservoir characterization including: rock physics analysis, Bayesian inference, pre-stack seismic inversion and geostatistical simulation of reservoir properties. Hydraulic units are geologic layers and zones characterized by similar properties of fluid flow in porous permeable media, and are defined at well locations based on logs, core measurements and production data. However, the number of wells available for hydraulic unit analysis is often extremely limited. In comparison, the lateral coverage and resolution of 3D seismic data is excellent, and can thus be used to extend hydraulic unit analysis away from well locations into the full 3D reservoir volume. We develop a probabilistic relationship between certain 3D seismic data attributes and the hydraulic units we determine at well locations. Since porosity and permeability distributions are estimated for each hydraulic flow unit as part of the process, we can use the 3D seismic probabilistic relationships to constraint geostatistical realizations of porosity and permeability in the reservoir, to be consistent with the flow unit analysis. Reservoir models jointly constrained by both 3D seismic data and hydraulic flow unit analysis can therefore help to improve the accuracy of dynamic reservoir flow simulation and production history matching.
Xiao, Liang (School of Geophysics and Information Technology) | Liu, Xiao-peng (Geological Exploration and Development Research Institute in Sichuan-Changqing Drilling and Exploration Engineering Corporation) | Mao, Zhi-qiang (State Key Laboratory of Petroleum Resources and Prospecting) | Zou, Chang-chun (School of Geophysics and Information Technology) | Hu, Xiao-xin (Geological Exploration and Development Research Institute in Sichuan-Changqing Drilling and Exploration Engineering Corporation, CNPC,) | Jin, Yan (Southwest Oil and Gas Field Branch Company, PetroChina)
The crossplot of porosity vs. Klinkenberg permeability (PERM) for 378 core samples, drilled from tight gas sands in the Xujiahe formation of the Anlu region-central Sichuan basin, southwest China, showed that tight-gas-sand permeability cannot be estimated effectively from porosity directly because only a poor relationship can be found between core-derived porosity and permeability because of the strong heterogeneity, especially for reservoirs with dominant microfractures (with porosities lower than 6.5%). However, the problem can be solved by introducing the HFU approach. In this paper, the 378 core samples were divided into five types on the basis of the difference of the flow-zone indicator (FZI), and then relationships of rock porosity and permeability were established for every type of core sample. By virtue of the analysis of the expression of FZI and the classical Schlumberger Doll Research (SDR) Center model, a novel technique used to obtain FZI from NMR logs was proposed and a corresponding model was established. The model parameters were calibrated by use of the laboratory NMR measurements of 54 plug samples taken from the Xujiahe formation. Carried out on the experimental data sets, this procedure can be extended to reservoir conditions to estimate consecutive formation permeability along the intervals through which NMR logs were acquired. The processing results of field examples illustrate that the calculated FZI values from field NMR logs match very well with the core analyzed results; the absolute errors among them are within the scope of 60.15. Moreover, permeability, estimated by use of the proposed technique and the core analyzed results are consistent. However, the calibrated SDR model is exclusive to the cases where formation permeability ranges from 0.2 to 0.4 md. To improve permeability prediction with the SDR model, many more core samples drilled from formations with dominant microfractures needed to be tested for laboratory NMR experiments to calibrate the SDR model for each HFU.
Zhang, Hao (Missouri University of Science & Tech) | Bai, Baojun (Missouri University of Science & Tech) | Song, Kai (Missouri University of Science and Technology) | Elgmati, Malek Mohamed (Missouri University of Science & Tech)
Shale gas reservoir developments have steadily increased over the past few years throughout North America. A significant amount of the produced gas in shales is stored in complex submicron pore structures. The absence of an intensive hydraulic flow unit (HFU) model for these shale gas source rocks makes the prediction of economic gas productivity and hydraulic fracturing risky. Therefore, understanding of pore size distribution, permeability, pore connectivity, and other petrophysical properties is crucial for accurate performance prediction and effective reservoir management. This study utilizes the dual-beam (SEM-FIB) instrument for shale gas tomography. The reconstructed 3D sub-micron pore model provides insights into the petrophysical properties of shale gas, including pore size distribution and porosity. These properties were used to define the shale gas hydraulic unit and permeability. The identified flow units were able to fit into existing flow unit models for unconventional reservoirs. The comparison between the proposed method and mercury injection capillary measurements (MICP) revealed similar data range however MICP method tends to slightly overestimate the flow unit. Flow simulation based on 3D Stokes equation using image segmentation was performed and consistent permeability value was found compared to the estimation in SEM-FIB tomography. However, the permeability simulation results tend to underestimate the permeability value in reality. A case example from Utica shale illustrated the use of this approach.
New oil fields have been recently discovered in remote areas of the Peruvian Amazonian rainforest. Heavy oil was tested in Cretaceous age reservoirs of Marañon basin: Casablanca, Vivian and Chonta. Conceptual development options for these fields are being analyzed; that is why, a proper characterization of rock-fluid properties of reservoirs is particularly important in this early stage of the asset life-cycle to optimize the field development strategy.
Several methodologies of reservoir characterization have been widely discussed in the literature; however not always interdisciplinary workflows are applied in data acquisition and processing for reservoir characterization. Study cases and lessons learned from studies carried out in a recently discovered heavy oil reservoir are discussed, as well as, the workflow applied to get representative SCAL data and integrated well test interpretation is described.
On the one hand, the process to achieve representative SCAL data includes sample selection criteria, SCAL program, and data validation to come up with a high quality data set to refine reservoir models. Sample selection technique used for this study is based on Global Hydraulic Elements (Corbett et al. 2004)5, which is combined with sedimentological studies, electrofacies logs and statistics from geo-cellular 3D model. This is aimed to select representative core plug samples for a cost effective lab program execution. On the other hand, routine core analysis data was integrated into well test interpretation to understand flow units and select near wellbore flow models that are consistent with petrophysical data. Finally, after processing and validating information, a selected set of data was incorporated into reservoir models.
The workflows discussed in this paper look forward to developing synergies in multy-disciplinary teams working on reservoir characterization, which could allow to improve the making decision process during early stage of the field.
Hossain, Zakir (Department of Environmental Engineering, Technical University of Denmark) | Fabricius, Ida L. (Department of Environmental Engineering, Technical University of Denmark) | Mukerji, Tapan L. (Stanford Center for Reservoir Forecasting, Stanford University) | Dvorkin, Jack (Stanford Rock Physics laboratory, Stanford University)
Naeeni, Mohammad Nazari (NIDC) | Zargari, Hadi (Petroleum University of Technology) | Ashena, Rahim (Petroleum University of Technology) | Ashena, Rahman (Islamic Azad University - Omidieh Branch) | Kharrat, Riyaz (Petroleum University of Technology)
Knowledge of permeability is critical for developing an effective reservoir description. Permeability data may calculated from well tests, cores and logs. Normally, using well log data to derive estimates of permeability is the lowest cost method. In the last years, the concept of hydraulic flow units (HFU) has been used in the petroleum industry to improve prediction of permeability in un-cored intervals/wells. This concept is strongly related to the flow zone indicator (FZI) which is a function of the reservoir quality index (RQI). Both measures are based on porosity and permeability of cores. It is assumed that samples with similar FZI values belong to the same HFU.
This paper will focus on the evaluation of formation permeability in un-cored intervals for a carbonate reservoir in Iran from core and well log data. We used Flow Zone Index method for rock type identification and Artificial Neural Networks for permeability estimation. Identifying the hydraulic flow units that is the first step of predicting permeability always takes lots of time and lack appropriate accuracy. We have developed a new clustering technique that is more precise, easy to apply and taking much less time.