Understanding fluid phase behavior is of primary importance in development of oil and gas reservoirs. It is well known that nanoporous confinement plays an important role in determining the phase behavior of fluids in confinement. While some quantitative understanding is available for how pure fluids behave in nanopores, the effects of surface chemistry are yet to be fully understood. This study investigates the effects of adsorbent surface chemistry on nanoporous confinement using silica-based MCM-41 adsorbents with different degrees of surface modification. Using a novel gravimetric apparatus, a quantitative comparison between the isotherms of n-butane and iso-butane was conducted using four different types of processed MCM-41 adsorbents. Three of the adsorbents possessed varying degrees of alkyl group surface modification with C1, C8, and C18 alkyl groups. The results of the adsorption tests in these three modified materials were compared to isotherms measured with pure, unmodified, MCM-41 adsorbent with a similar pore size. Comparison between the isotherms of n-butane and iso-butane provided deeper insight to the elements that influence the changes in confinement-induced phase behavior and whether the structure of the isomeric adsorbate molecules has significant impact on adsorption.
Crushed shale is commonly used to characterize rock and fluid properties. However, in kerogen-rich rocks, this form of core analysis may not fully account for the effects of nanoporosity and flexibility of the kerogen. To explore the effects of both, we studied capillary condensation and evaporation in crushed, kerogen-rich samples from a shale gas reservoir in the Middle East using repeated injections of n-butane and n-pentane.
A novel, gravimetric capillary condensation apparatus was used to measure isotherms for both n-butane and n-pentane in the crushed samples at temperatures from 4.9 to 65.6°C. The wide temperature range employed allowed us to compare the properties determined at the lower and higher temperatures. Repeated measurements were then used to characterize the consistency of the rock and fluid properties.
Adsorption-and-desorption hysteresis was observed at all temperatures for both fluids. Therefore, routine and special core analysis measurements made during pressure increase may not accurately approximate the pore-fluid occupancy of the reservoir during pressure decrease (i.e., production). Furthermore, it was found that the measured properties were repeatable throughout the n-butane isotherms, while the pore size and fluid density fluctuated during the n-pentane measurements due to irreversible swelling of the kerogen. Thus, core analysis procedures carried out at temperatures different from the reservoir temperature using fluids with compositions dissimilar to that of the reservoir fluid may also result in inaccurate determinations of the rock and fluid properties further introducing significant uncertainties into reservoir performance evaluations.
Although nanopores are known to depress the phase changes of fluids in synthetic media, little is known about their effects on rock and fluid properties at reservoir conditions. This study presents evidence that the phase boundaries of fluids in kerogen-rich shale may significantly differ from those of unconfined fluids in the bulk. We use those measurements to demonstrate the complexities associated with employing the Barrett-Joyner-Halenda technique, which is commonly used for determining the pore size distribution, in shale rock.
He, Lang (Southwest Petroleum University, Chengdu) | Mei, Haiyan (Southwest Petroleum University, Chengdu) | Hu, Xinrui (Southwest Petroleum University, Chengdu) | Dejam, Morteza (University of Wyoming) | Kou, Zuhao (University of Wyoming) | Zhang, Maolin (Yangtze University, Wuhan)
Lang He, Haiyan Mei, and Xinrui Hu, Southwest Petroleum University, Chengdu; Morteza Dejam and Zuhao Kou, University of Wyoming; and Maolin Zhang, Yangtze University, Wuhan Summary A series of shale gas adsorption and desorption experiments are conducted. Desorption and adsorption curves are not coincident, with the former located above the latter, which suggests that adsorption hysteresis also occurs in shale gas. Pseudodeviation factor (Z*) is revised to advance the material-balance equation (MBE) and flowing material balance (FMB). The case study of the Fuling Shale in China illustrates that original gas in place (OGIP) of all three wells (1-HF, 2-HF, and 3-HF) calculated by conventional FMB is lower than that calculated by refined FMB, which has accounted for adsorption hysteresis. Adsorption hysteresis should be accounted for to accurately determine OGIP.
Summary In this work, we propose an ensemble-based seismic history matching approach to predict reservoir properties, i.e. porosity and permeability, with uncertainty quantification, using both production and time lapse seismic data. To avoid the common underestimation of uncertainty in ensemblebased optimization approaches, and to make the computation feasible, we introduce the convolutional autoencoder to reparameterize seismic data into a lower dimensional space. We then apply the Ensemble Smoother with Multiple Data Assimilation to optimize an ensemble of reservoir models using the production and re-parameterized seismic data. The proposed methodology is tested on a 2D synthetic case. The inversion results indicate that the method can largely improve the characterization of reservoir models compared to the history-matching scenario with production data only.
In this work, we focus on a Bayesian inversion method for the estimation of reservoir properties from seismic data and we study how the inversion parameters, such as rock-physics and geostatistical parameters, can affect the inversion results in terms of reservoir performance quantities (pore volume and connectivity). We apply a Bayesian seismic inversion based on rock-physics prior modeling for the joint estimation of facies, acoustic impedance and porosity. The method is based on a Gibbs algorithm integrated with geostatistical methods that sample spatially correlated subsurface models from the posterior distribution. With the ensemble of multiples scenarios of the subsurface conditioned to the experimental data, we can evaluate two quantities that impact the production of the reservoir: the reservoir connectivity and the connected pore volume. For each set of parameters, the inversion method yields different results. Hence, we perform a sensitivity analysis for the main parameters of the inversion method, in order to understand how the subsurface model may be influenced by erroneous assumptions and parameter settings.
Presentation Date: Monday, October 15, 2018
Start Time: 1:50:00 PM
Location: 206A (Anaheim Convention Center)
Presentation Type: Oral
Summary In this work, we propose a stochastic nonlinear inversion framework for PP and PS seismic data based on the ensemble smoother with multiple data assimilations (ES-MDA) to estimate elastic reservoir properties with uncertainty quantification. The ES-MDA is an iterative ensemble-based data assimilation method that generates an ensemble of solutions of the inverse problem. In our approach, it is applied to a seismic inversion problem in which the full Zoeppritz equations, without linearization, are used to improve the inversion accuracy. The ensemble of updated reservoir realizations obtained by assimilating seismic data allows evaluating the associated model uncertainty. To avoid the model uncertainty be underestimated in the ensemble-based approach, we propose to apply the ES-MDA in a lower-dimensional data space obtained by the re-parameterization of PP and PS seismic data using the singular value decomposition (SVD).
We carry out the inversion of marine controlled-source electromagnetic data using real coded genetic algorithm to estimate the isotropic resistivity. Unlike linearized inversion methods, genetic algorithms belonging to class of stochastic methods are not limited by the requirement of the good starting models. The objective function to be optimized contains data misfit and model roughness. The regularization weight is used as a temperature like annealing parameter. This inversion is cast into a Bayesian framework where the prior distribution of the model parameters is combined with the physics of the forward problem to estimate the aposteriori probability density function in the model space. The probability distribution derived with this approach can be used to quantify the uncertainty in the estimation of vertical resistivity profile. We apply our inversion scheme on three synthetic data sets generated from horizontally stratified earth models. For all cases, our inversion estimated the resistivity to a reasonable accuracy. The results obtained from this inversion can serve as starting models for linearized/higher dimensional inversion.
Presentation Date: Monday, October 15, 2018
Start Time: 1:50:00 PM
Location: Poster Station 13
Presentation Type: Poster
Tseng, H.-W., J. Stalnaker, L. M. MacGregor, and R. V. Ackermann, 2015, Multidimensional analyses of the SEAM controlled source electromagnetic data--the story of a blind test of interpretation workflows: Geophysical Prospecting, 63, 1383-1402, https://doi.org/10.1111/1365-2478
The variations of dynamic reservoir properties cause the change in seismic response. During the production phase, time-lapse seismic data can be used to monitor water saturation and pressure changes. The prediction of water saturation and pressure conditions from seismic datarequires physical model to link their changes to variations in elastic properties. The empirical models commonly used constant empirical coefficients in the reservoir. However, in first part of the work, we show that different porosity, saturation, and pressure in in-situ conditions can affect the model coefficients. We then propose a new rock physics model to compute the changes in reflectivity due to thechanges in saturation and pressure, accounting in-situ reservoir conditions. The model is then integrated in a Bayesian inversion method to predict water saturation and pressure changes directly from the amplitude difference of time-lapse seismic data. We apply the proposed method to a synthetic dataset and obtain accurate results.
Presentation Date: Tuesday, October 16, 2018
Start Time: 8:30:00 AM
Location: 209A (Anaheim Convention Center)
Presentation Type: Oral
In this paper we propose a new workflow to perform Petrophysical Joint Inversion (PJI) of surface to surface seismic and Controlled Source ElectroMagnetic (CSEM) data, to recover reservoir properties (clay volume, porosity and saturation). Seismic and CSEM measurements provide independent physical measurements of subsurface that complement each other. In the case of well-logs, the basis of the PJI training dataset, taking advantage of such complementarity is straightforward. Indeed, elastic and electric measurements of earth properties sense the same earth volume at much the same scale. When applying the training dataset to the surface data derived geophysical attributes, the order of magnitude gap in between the scale at which those elastic and electric attributes represent the earth undermines dramatically PJI validity. Various CSEM inversion constraining methods (regularization breaks, prejudicing, use of an a priori model etc) help to reconcile seismic and CSEM resolution, but they are usually proven to be insufficient or inaccurate. In addition to these methods, we suggest adding a further downscaling step, so the recovered electric attribute resolution can be adequate with respect to the seismic one, hence fit for purpose. Such downscaling is designed to be consistent in electrical attribute space via transverse resistance within a rockphysics framework. The workflow will be demonstrated on a case study.