|Theme||Visible||Selectable||Appearance||Zoom Range (now: 0)|
Wang, Lei (Nazarbayev University) | Liu, Mingliang (University of Wyoming) | Altazhanov, Arlybek (Nazarbayev University) | Syzdykov, Bekassyl (Nazarbayev University) | Yan, Jiang (Xinjiang Oilfield) | Meng, Xin (Xinjiang Oilfield) | Jin, Kai (Jiangxi Shale Gas Institute)
Accurate calculation of adsorbed shale gas content is critical for gas reserve evaluation and development. However, gas adsorption and desorption experiments are expensive and time-consuming, while physics-based models and empirical correlations are unable to accurately capture the adsorption characteristics for different shales. Langmuir adsorption is one of the most commonly used model for calculating the adsorbed gas content in shale gas reservoirs. However, most existing correlations for the Langmuir pressure and Langmuir volume in the model are oversimplified based on limited experimental data points. Thus they are not representative of key geological parameters and are far from accurate for prediction in many cases. We developed a variety of machine learning models that are multivariable controlled to quantify shale gas adsorption.
The data-driven method subdivides into two procedures: data compilation and machine learning regression. Over 700 data entries, composed of reservoir temperature (T, °C), total organic carbon (TOC, wt%), vitrinite reflectance (Ro,%), Langmuir pressure, and Langmuir volume are compiled from shale gas plays mainly in USA, Canada, and China. Data have been consistently curated, then machine learning approaches, including multiple linear regression (MLR), support vector machine (SVM), random forest (RF) and artificial neural network (ANN), have been built, trained and tested by partitioning the data into 75%:25%. For SVM, RF and NN models, 1000 simulations were run and averaged for performance comparison.
MLR identifies non-negligible parameters and general trends for shale gas adsorption. Nonetheless, the correlation coefficients from MLR are far from satisfactory. For Langmuir pressure, RF models fit best to the data entries and the other models follow the order of SVM > ANN > MLR. Particularly, RF models show the highest performance stability with the averaged R-squared value of 0.84 and the maximum of 0.87, indicating a very strong relationship constructed for these 213 data entries. For 485 Langmuir volume data entries, RF models also perform best while the other three regression methods are comparable. It should be noted that altering machine learning model structure and parameters could significantly affect the regression results.
Robust and universal machine learning models for estimating adsorbed shale gas content with high confidence level are established, which not only provide more accurate estimation and broader parameter adaptation than physics-based and empirical models, but also circumvent the high-cost and time-consuming deficiency of experimental measurements. These machine learning models can be used to estimate adsorbed gas content for shale plays with limited experimental measurements. Moreover, they can be incorporated into reservoir simulators to improve the simulation performance.
Ahmed, Shehzad (Universiti Teknologi PETRONAS) | Elraies, Khaled Abdalla (Universiti Teknologi PETRONAS) | Hanamertani, Alvinda Sri (University of Wyoming) | Hashmet, Muhammad Rehan (Nazarbayev University) | Shafian, Siti Rohaida Mohd (PETRONAS Research Sdn Bhd) | Hsia, Ivy Chai Ching (PETRONAS Research Sdn Bhd)
The application of CO2 foam has caught overwhelming attention for fracturing shales. In applications, high foam deterioration and insufficient viscosity at operating conditions are the major concerns associated with foam fracturing process. In this study, polymer-free CO2 foam possessing high stability has been presented through chemical screening and optimization under HPHT conditions. Initial screening was performed by conducting a series of foam stability experiments considering different commercial anionic surfactants, concentration, and foam stabilizer addition using FoamScan instrument. Foam rheology study was then performed by considering the similar investigated factors under fracturing conditions using HTHP foam rheometer. All the tested solutions were prepared in fixed brine salinity and HPAM polymers with different molecular weights were used in evaluation of the performance of the designed polymer-free foam in term of foam strength. In comparison with other types of surfactant, alpha olefin sulfonate (AOS) exhibited the best foam stability and viscosity at testing conditions. The optimum AOS concentration providing the best performance was found to be 5000 ppm and its combination with 5000 ppm of foam booster (betaine) further increased AOS foam longevity. An improved result on foam stability and viscosity was not obtained by increasing surfactant concentration. Results on foam rheology reveals that CO2 foam generated in the presence of different molecular weight classical HPAM polymers could not provide significant increment in foam viscosity under experimental conditions. It was observed that these types of polymer underwent degradation due to some unfavorable mechanisms which will be expected to negatively affect its performance during fracturing process. On the other hand, polymer-free CO2 foam was found to produce a higher stability and relatively equally high viscosity compared to polymer-stabilied CO2 foam without experiencing degradation at high pressure and temperature conditions. Therefore, based on this study, it is recommended to use polymer-free foam for fracturing shales application. The use of formulated polymer-free CO2 foam which has high stability and viscosity will lead to improved fracture cleanup, minimized formation damage and pore plugging, and efficient proppant placement which will ultimately enhance gas recovery from unconventional shales.
Barsotti, Elizabeth (University of Wyoming)
Shale reservoirs are estimated to account for approximately 10-30% of oil and gas worldwide, yet operators rarely produce more than 10% of the original hydrocarbons in place from them. These poor production numbers are a result of the assumption that the same pressure-volume-temperature (PVT) analysis procedures that are employed in conventional reservoirs are also applicable to shale and tight reservoirs. However, traditional PVT analysis does not account for the nanoporosity of the shale and, therefore, neglects the ability of nanopores to significantly alter the phase behavior of reservoir fluids. To quantify the effects of shale nanoporosity on the phase behavior of reservoir fluids, a novel gravimetric apparatus was developed. Unlike other gravimetric apparatuses in the literature, ours is compatible with both simple and complex experimental fluids and up to several hundred grams of unconsolidated or consolidated porous media at temperatures and pressures up to 232ᵒC and 5,000 psi, respectively. Furthermore, our apparatus does not require a buoyant force correction, which is one of the major shortcomings of most commercially available gravimetric apparatuses. These unique features allow us to study fluid phase behavior in shale and tight cores with high accuracy and efficiency. In the course of an exhaustive three-year research program, we have used this apparatus to measure the first capillary condensation isotherm for a fluid mixture with more than two components and discovered new phenomena of capillary condensed and supercritical fluids in the nanopores of shale rock and synthetic porous media. By reviewing the works produced over the course of this research, we are now able to answer longstanding questions as to when and how nanoconfinement-induced phase behavior occur in shale reservoirs and the implications that different types of phase behavior, including capillary condensation and nanoconfined supercriticality, have for oil and gas production.
Chaisoontornyotin, Wattana (University of Wyoming) | Mohamed, Abdelhalim (University of Wyoming) | Bai, Shixun (University of Wyoming) | Afari, Samuel Asante (University of Wyoming) | Mirchi, Vahideh (University of Wyoming) | Recio, Antonio (Halliburton) | Pearl, Megan (Halliburton) | Piri, Mohammad (University of Wyoming)
This work investigates the impact of fracture surface area to rock volume ratio (Af/Vr) on spontaneous imbibition at ambient conditions using ultra-tight reservoir carbonate rocks. A significantly improved insight is presented into the physics of surfactant-based enhanced oil recovery from tight and fractured formation rocks. The performance of a blank brine (2.0 wt.% KCl) and an engineered surfactant solution are compared. This work uses custom-built high-accuracy Amott cells (0.01cc resolution) to precisely measure the produced oil from the tight rock samples. Interfacial tension/contact angle (IFT/CA) measurement systems and a focused ion beam-scanning electron microscope (FIB-SEM) are used to measure interfacial tension (IFT) between the fluid phases and characterize rock samples, respectively. The measurements are then used to explain the observed recovery trends. The results reveal that volume of oil produced to volume of oil in place ratio (recovery factor, Rf) increases with increasing Af/Vr ratio before reaching a plateau. This suggests that there is a threshold Af/Vr value beyond which an increase in Af/Vr will not result in any incremental recovery for a given rock/brine/oil system. It is observed that the threshold Af/Vr value varies with the brine composition. The ultimate oil recovery is higher for all tested Af/Vr values when the surfactant is deployed. The results discussed herein can enhance the design of fracturing, the fluids, and additive packages used in hydraulic fracturing operations in unconventional oil reservoirs and is expected to help reduce associated cleanup times and costs.
Tight reservoirs are an important group of hydrocarbon-bearing geologic systems that are known to contain a significant amount of oil and gas. Production of hydrocarbons from these reservoirs, however, remains a challenge due to their low porosity and permeability. Therefore, economic development of tight reservoirs depends on the presence of fractures that can improve hydraulic conductivity of the medium. To effectively enhance the production in fractured reservoirs, the fracturing operations must be carefully optimized. The created surface area of hydraulic fractures is one of the most important parameters that significantly impacts well productivity.
Ng, K. (University of Wyoming) | Yu, H. (University of Wyoming) | Wang, H. (University of Wyoming) | Kaszuba, J. (University of Wyoming) | Alvarado, V. (University of Wyoming) | Grana, D. (University of Wyoming) | Campbell, E. (Wyoming State Geological Survey)
ABSTRACT: The Rock Springs Uplift (RSU) Wyoming was characterized for a long-term CO2 storage due to its proximity to a large CO2 emissions point source, the overall structural geometry, and geologic formations. Rock cores of the Weber Sandstone formation and the dolomite facies of the Madison Limestone formation from the RSU were prepared into 25-mm diameter rock samples. These samples were saturated with brine, aged with brine, and aged with CO2-rich brine. Laboratory hydrostatic and triaxial experiments were performed at in-situ reservoir conditions with the temperature of 90°C and three differential pressures of 6.9 MPa, 34.5 MPa and 55.2 MPa. This paper presents the geomechanical results of the brine-saturated Weber Sandstone samples. A hypothetical reasoning approach was used to describe the effect of physical properties (microstructure) on the stress-strain behavior of these rocks and explain anomalies observed in the geomechanical results. The anomalies were attributed to the closure of initial compliant pores that induced new cracks from their tips and the filling of pore fluid in these crack spaces. This phenomenon changes the microstructure, decreased the bulk volume, and underestimated the initial compliant porosity of the sandstone. This phenomenon continued to influence the stress-strain behaviors during the triaxial loading stage.
To successfully implementing geologic carbon sequestration, it is essential to reliably and economically predict the “permanence” of CO2 in potential storage sites (Wilson et al., 2003). The technology for injecting CO2 into deep geological formations already exists and has been applied for enhanced oil recovery and acid gas disposal (Viswanathan et al., 2008). To become a viable option for a geologic sequestration, potential storage sites must be assessed to determine if they can store much larger amounts of CO2 over much greater periods of time (Viswanathan et al., 2008). The challenge derives in part from the complex, heterogeneous nature of the geologic storage systems. Without an acceptable understanding of the ultimate fate and permanence of CO2 in these storage sites, risk mitigation based on the integrity of a CO2 reservoir cannot be achieved. Significant strides have been made towards achieving this goal, most notably in our understanding of the physics of flow of CO2 in porous media, and the geochemical and mineralogical changes imposed by CO2 in a reservoir. One of the knowledge gaps is our limited ability to understand, measure, and predict the geomechanical effects of CO2 injection into the subsurface. Without adequate ability to measure and predict these subsurface geomechanical effects, assessment and mitigation of storage risks or protection of groundwater, natural resources, and the public cannot be fully accomplished.
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)
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. The conventional FMB underestimates OGIP of the three wells by 2.21, 3.29, and 4.02%, respectively. Adsorption hysteresis should be accounted for to accurately determine OGIP.
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.
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.
Presentation Date: Tuesday, October 16, 2018
Start Time: 8:30:00 AM
Location: 209A (Anaheim Convention Center)
Presentation Type: Oral
We carry out the two-dimensional inversion of marine controlled-source electromagnetic data from the SEG advance modeling program using MARE2DEM Software.We applied this inversion on three survey lines from the given data set to image the salt body and delineate thin hydrocarbon reservoirs that are present near the salt flanks.The inversion was unconstrained and did not use any a priori information about the salt body from the seismic imaging or nearby well logs. Despite the complex 3D structure of thesalt model, our inverted results agree well with the truemodel demonstrating the robustness of the method in imaging the reservoirs and their lateral extents without any prior information.
Presentation Date: Tuesday, October 16, 2018
Start Time: 9:20:00 AM
Location: Poster Station 15
Presentation Type: Poster
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