During development of the Eagle Ford unconventional resource near the San Marcos Arch, a non-productive mudstone associated with drilling issues was identified between the primary Eagle Ford producing zone and the underlying Buda Limestone. As the top of the Buda typically exhibits evidence of karsting but is unaltered when overlain by this mudstone, and the mudstone contains higher abundances of clay than the Eagle Ford, two questions were posed: (1) Does this mudstone represent a depositional system separate from the Eagle Ford and (2) does it act as a fracture barrier between the Eagle Ford and underlying water-bearing rocks?
The current study analyzed two cores from Lavaca and Fayette counties, which included petrographic, XRD, and geomechanical (point-load penetrometer and micro-rebound hammer) analyses to determine the mineralogy and geomechanical properties of the mudstone, the Eagle Ford, and the Buda. Logs from 345 wells within a six-county were used to correlate and map four horizons associated with the mudstone. These results were integrated with an earlier core study that included biostratigraphic, petrographic, XRD, and XRF analyses, and regional log correlations across the arch into the Brazos Basin.
The geomechanical tests found that the mudstone is significantly weaker than the overlying Eagle Ford, averaging 32% lower calculated unconfined compressive strength (UCS) values derived from the penetrometer and 36% lower using the micro-rebound hammer. Higher clay and lower calcite abundances within the mudstone are responsible for its lower strength; the XRD analyses found that the shale samples from the mudstone contained an average of 47% clay, whereas the Eagle Ford marls contained an average of 34% clay. The petrographic analyses found that the clay is concentrated in structureless layers that are interpreted to represent fluid-mud deposits associated with hypopycnal plumes.
The biostratigraphic study identified Early Cenomanian markers associated with the Maness Shale of East Texas which lies between the Woodbine and Buda, in agreement with the regional cross-sections which correlated the mudstone to the Maness. A hot gamma ray spike produced by a phosphatic lag at the top of the mudstone was key to the correlations. Thickness trends of the Maness differ considerably from the Eagle Ford; it has a distinct northeast-southwest trend and pinches out in southern Karnes County, suggesting that it was a depositional system unrelated to the Eagle Ford.
Comparison of Maness thicknesses with cumulative first year oil and water production data from over 2000 horizontal wells in the study area found a significant correlation between Maness thickness and water/oil ratios. In particular, there is a 50% decrease in water/oil ratios between Maness thicknesses of 5 to 10 ft, (1.5-3 m) suggesting that the Maness may be acting as a fracture barrier where it is >10 ft (3 m) thick.
The ensemble based methods (especially various forms of iterative ensemble smoothers) have been proven to be effective in calibrating multiple reservoir models, so that they are consistent with historical production data. However, due to the complex nature of hydrocarbon reservoirs, the model calibration is never perfect, it is always a simplified version of reality with coarse representation and unmodeled physical processes. This flaw in the model that causes mismatch between actual observations and simulated data when ‘perfect’ model parameters are used as model input is known as ‘model error’. Assimilation of data without accounting for this model error can result in incorrect adjustment to model parameters, underestimation of prediction uncertainties and bias in forecasts.
In this paper, we investigate the benefit of recognising and accounting for model error when an iterative ensemble smoother is used to assimilate production data. The correlated ‘total error’ (combination of model error and observation error) are estimated from the data residual after a standard history matching using Levenberg-Marquardt form of iterative ensemble smoother (LM-EnRML). This total error is then used in further data assimilations to improve the model prediction and uncertain quantification from the final updated model ensemble. We first illustrate the method using a synthetic 2D five spot case, where some model errors are deliberately introduced, and the results are closely examined against the known ‘true’ model. Then the Norne field case is used to further evaluate the method.
The Norne model has previously been history matched using the LM-EnRML (
The field development phase prior to investment sanction is characterized by relatively large uncertainties at the time important decisions have to be made. It is, for instance, crucial to select an appropriate recovery strategy (depletion or injection) to obtain optimal hydrocarbon cumulative production whilst ensuring good profitability of the project. Evaluation of reservoir as well as economic uncertainties and quantification of their impact are needed before the field development concept selection.
This paper describes how to stochastically assess reservoir and economic uncertainties and the screening process used to select the best recovery strategy. The chosen methodology is the combination of uncertainty studies, including both continuous, discrete and controllable parameters. The different screened scenarios are combined in a stochastic decision tree, built-up through decision and chance nodes, to establish a distribution of recoverable volumes and rank the recovery strategies given a chosen criterion. A second uncertainty study is performed by adding economic uncertainties to the initial set of reservoir uncertain parameters. Eventually a new decision tree is established and scenarios ranked using economic criteria.
The application of this methodology to an oil field from the Norwegian continental shelf and how recovery strategies are ranked are presented in this paper. The described methodology has exhibited the risks and uncertainties carried by the project, as it was possible to rank the different solutions based on the dispersion of the recoverable volumes distribution and/or on the net present value (NPV). In the context of a marginal or large capex project, a robust P90 case is required and this may therefore influence the choice of the recovery strategy. For instance, a scenario yielding the largest hydrocarbon volume may not be selected because it requires too many wells and/or too large investment if one of these criteria is defined as the most important. In addition, the combination of uncertainty studies enabled a full economic evaluation covering the entire recoverable volumes distribution whereas in many projects economic evaluation is focused on the P90, Mean and P10 scenarios.
The two-step integrated approach allows a decision to be made whilst taking into account both reservoir and economic aspects. Having a combined stochastic approach to the reservoir and economic uncertainties avoids a biased decision. All cases are stochastically covered and screened using a systematic and unified methodology that gives the same weight to each scenario.
Ensemble-based methods are among the state-of-the-art history matching algorithms. In practice, they often suffer from ensemble collapse, a phenomenon that deteriorates history matching performance. To prevent ensemble collapse, it is customary to equip an ensemble history matching algorithm with a certain localization scheme. Conventional localization methods use distances between physical locations of model variables and observations to modify the degree of observations' influence on model updates. Distance- based localization methods work well in many problems, but they also suffer from some long-standing issues, including, for instance, the dependence on the presence of physical locations of both model variables and observations, the challenges in dealing with nonlocal and time-lapse observations, and the non-adaptivity to handle different types of model variables. To enhance the applicability of localization to various history matching problems, we propose to adopt an adaptive localization scheme that exploits the correlations between model variables and observations for localization. We elaborate how correlation-based adaptive localization can mitigate or overcome the noticed issues arising in conventional distance-based localization.
To demonstrate the efficacy of correlation-based adaptive localization, we apply it to history-match the real production data of the full Norne field model using an iterative ensemble smoother (iES), and compare the history matching results to those obtained by using the same iES but with distance-based localization. Our study indicates that, in comparison to distance-based localization, correlation- based localization not only achieves close or better performance in terms of data mismatch, but also is more convenient to implement and use in practical history matching problems. As a result, the proposed correlation-based localization scheme may serve as a viable alternative to conventional distance-based localization.
Optimizing drainage strategy is an important part of petroleum reservoir management, and has to be implemented as a dynamic process. This work describes a unique combination of standalone and coupled reservoir simulation modeling as well as production decline curve analysis and tracer data interpretation to establish optimal gas injection strategy in Smørbukk and Smørbukk Sør fields in the Norwegian Sea for improved reservoir management in terms of reserves and economy. The drainage strategy challenge of Smørbukk and Smørbukk Sør fields is how to prioritize gas export from the fields and how to allocate the remaining gas in the efficient injectors for enhanced condensate recovery. The results triggered a change in injection strategy to rank gas disposition internally in optimized locations both in Smørbukk and Smørbukk Sør fields, and to split the injection between the two fields. The results showed that gas-oil-ratio development of producers, gas injection efficiency (GIF), well to well tracer communication and produced water-gas ratio development are the key factors. Reservoir segments with lower produced gas-oil ratio and higher water-gas ratio development have shown the most favorable locations for gas injection. It is demonstrated also that injection efficiency decreases by time; accordingly the drainage strategy consists in two phases: Optimized gas cycling period into efficient injectors in Smørbukk and Smørbukk Sør fields and a blowdown phase including converting injectors to producers when the injection is not efficient. The work addresses the workflow and methodology for drainage strategy, main challenges and related lessons associated to reservoir simulation results, compared with production data and tracer results. The paper will shed light on the future of reservoir management and forecasting of drainage strategy for matured complex fields.
This work describes a methodology that evaluates the Discrete Latin Hypercube with Geostatistical Realizations (DLHG) sample size for complex models in the history matching under uncertainties process with application to the Norne Benchmark Case. The sample size affects the time demanded and results accuracy in a history matching process because a small sample size can yield inaccurate risk quantification and a high sample size can demand excessive time to reach good results. Both factors should be evaluated in order to improve the project's efficiency and to obtain reliable results. Such evaluation gains greater importance in complex reservoir models because the number of tests to determine the reservoir scenarios that match dynamic data can be high due to the level of complexity. The methodology presented in this work is divided in three steps. First, we evaluate the ability of DLHG to produce output cumulative distribution functions (CDF) that replicate a more exhaustive sampling technique (Monte Carlo) using the Kolmogorov-Smirnov test. The output is the misfit between observed and simulated production rates; then, we compare the influence and correlation matrices obtained with DLHG and Monte Carlo samples. The influence matrix shows the impact of the uncertainty variation on the outputs and the correlation matrix measures the strength of the dependence between the uncertainty attributes and outputs. Finally, we perform the stability test. The methodology was applied to the Norne benchmark case; a field located in the Norwegian Sea. The main characteristics of the methodology are: (1) it uses a statistical technique to compare the output CDFs from the reference and DLHG samples and (2) it evaluates the ability of the DLHG sample to identify the reservoir attributes that affect the history match results. We evaluated DLHG sample sizes of 20, 50, 100 and 200, and considered a MC sample size of 5,000 to the Norne benchmark case. The DLHG CDFs for the 100 sample size was able to accurately replicate the corresponding MC CDFs, however it did not replicated the behavior of the influence and correlation matrices. The DLHG sample size of 200 was able to reproduce the CDFs outputs, the influence and correlation matrices and it was considered stable. The study showed that even if the sample size is able to represent the CDFs outputs from a reference solution, the influence and correlation matrices should be evaluated. The methodology presented can be incorporated into usual history match routines.
The Markov chain Monte Carlo (McMC) stochastic approach is widely used to estimate subsurface properties. However, estimating uncertainty quantitatively is also very important when performing stochastic inversion. Therefore, the goal of this paper is to apply the transdimensional, or reversible jump, McMC (rjMcMC) method to obtain a 3-D seismic impedance model and to determine a corresponding uncertainty cube by estimating the standard deviation of the models that are included in the Markov chains. By combining the uncertainty volume and impedance models, we can estimate the acoustic impedance and the uncertainty of the layer boundary location. The uncertainty can also be related to the magnitude of velocity discontinuity. To demonstrate the performance and reliability of the rjMcMC inversion, we used the seismic data from the E-segment of Norne field in Norwegian Sea. The results of transdimensional McMC inversion show high velocity contrasts nearby gas-oil contacts and high uncertainty near discontinuities.
Presentation Date: Wednesday, September 27, 2017
Start Time: 8:55 AM
Presentation Type: ORAL
Yong, Li (Research Institute of Petroleum Exploration and Development, PetroChina) | Baozhu, Li (Research Institute of Petroleum Exploration and Development, PetroChina) | Qi, Wang (Research Institute of Petroleum Exploration and Development, PetroChina) | Xingliang, Deng (Tarim Oilfield Company, PetroChina)
Multi-scale pores, fractures, vugs and caves are developed in the naturally fractured-vuggy carbonate gas condensate reservoirs in China. How to properly forecast the performance of this kind of reservoirs based on reservoir simulation is a major challenge. This paper documents some of the key elements involved in construction of a representative and equivalent numerical model for this kind of reservoirs.
Base on the characteristic of fractured-vuggy gas condensate reservoirs in China, different kind of reservoir patterns are identified. And three equivalent simulation methods are proposed in order to characterize the complex vug-fracture-cave architecture. Take T1 fractured-vuggy carbonate gas condensate reservoir in China for example. The deterministic modeling and simulation method is used for large cave pattern reservoir. While for the other kinds of reservoirs, different simulation models include fine single porosity model, dual porosity model with different modeling method. And the different models are all built in both structured and unstructured grid, among which the single porosity fine unstructured grid model is considered as the correct solution. But simulation time of the fine single porosity model is too long to use for the practical full filed simulation. While it brings certain errors if the other models used compared to the fine single porosity model, and dual porosity model with structured gridding are selected as the equivalent simulation method. Then through matching the simulation results of fine single porosity model, the pseudo-relative-permeability curve, the pseudo-capillary-pressure curve and the composition transport coefficients are established for the dual porosity model. Finally, the generated pseudo-curves are applied for the equivalent simulation of full field T1 reservoir.
This equivalent simulation method has been successfully applied to T1 carbonate gas condensate reservoir, which are verified by the actual reservoir performance. It also provide a method for reservoir development strategy optimization, which are also used in T1 optimization development plan later.
History matching integrated with uncertainty reduction is a key process in the closed loop reservoir development and management methodology which is used for decision analysis related to the development & management of petroleum fields. Despite developments over the last decades in history matching & uncertainty analysis, the challenge of capturing complex interaction among several attributes and several reservoir responses acting simultaneously for complex models still remains. This paper describes the use of a probabilistic and multi-objective history matching integrated with uncertainty reduction as a systematic and iterative process for obtaining a set of reservoir models that honors dynamic data in a complex field case. The methodology is an iterative process that simultaneously matches different objective functions, one for each well production profile. The procedure uses a re-characterization step, where the uncertainties of the attributes (represented by their probability density functions) are updated using indicators that show global and local problems and a correlation matrix to capture the interaction between several reservoir uncertainties and the different objective functions. The methodology was applied to the Norne Field benchmark case considering production data up to 2001 and the remaining part of the provided history is used to estimate the quality of production forecast. The major benefit derived from the application of the methodology was the identification of global and local problems. The initial reservoir models presented high discrepancies between simulated and observed data. The use of independent objective functions in conjunction with the concise plot that is based on the normalized quadratic error of each production data highlighted when new parametrization of the reservoir was necessary. New reservoir attributes were added, such as separated permeability curves for each reservoir formation and new gas permeability curves that better describe the fluid behavior. The initial number of uncertain attributes was twenty seven; the correlation matrix clearly showed which one of those had major influence on the results. Some attributes with significant impact in the study were water-oil and gas-oil contact and faults transmissibility. We updated the probability of the most influencing attributes in order to identify the uncertain levels that improved the history match results. The methodology integrated the process of history matching with uncertainty analysis, addressing both processes simultaneously for a complex case. The methodology was effective and simple to use, even in the complex case study where the reservoir characterization is important.
We propose to apply a transdimensional inversion algorithm, reversible-jump Markov chain Monte Carlo (rjMCMC), to seismic waveform inversion to characterize reservoir impedance and estimate uncertainty using post-stack data. This method can help to automatically determine a proper parameterization, specifically an optimal number of layers for a given data set and earth structure. The rjMCMC can also enhance uncertainty estimation since its transdimensional sampler can prevent a biased sampling of model space, including the number of unknowns. An ensemble of solutions with different parameterizations can statistically reduce the bias for parameter estimation and uncertainty quantification. Our results show that the inversion uncertainty, which includes uncertainty in both properties and their locations, is related to the contrast in properties across an interface. That is, there is a trade-off between property uncertainty and location uncertainty. A larger discontinuity will cause more uncertainty in model property values at the location of the interface, but less uncertainty in its location. Therefore, we propose to use the inversion uncertainty as a novel attribute to facilitate delineation of subsurface reflectors and quantify the magnitude of discontinuities.
Presentation Date: Wednesday, October 19, 2016
Start Time: 8:50:00 AM
Presentation Type: ORAL