The traditional definition of volumetric sweep efficiency sums the effects of both fingering (arising due to contrasts in mobility) and bypassing (arising due to contrasts in permeability as well as well placement). Accordingly, we cannot quantitatively attribute poor sweep to either bypassing or fingering. Similarly, in EOR, the incremental recovery cannot be quantitatively associated with the reduction of those effects. For such purposes, we rely on visualization and mapping of saturation profiles to quantify and characterize the remaining oil in place including its distribution. . In this work, we propose a complementary method to obtain an instantaneous insight of the remaining oil distribution. We demonstrate the decomposition of fingering and bypassing effects and its utility. We first redefine recovery factors such that we decouple bypassing and fingering effects. We then validate those redefined sweep indicators by examining a 5-spot waterflood and two idealistic polymer floods. Later, we demonstrate the possible utility of those redefined sweep indicators through different examples. In one example, we compare the performance of a shear - thinning polymer to a recovery-equivalent Newtonian polymer. Using fingering and bypassing sweep indicators, we can immediately conclude that the shear-thinning polymer exacerbates bypassing. We recommend the adoption of our redefined sweep indicators in any simulation suite. They provide instant understanding of sweep and hence can be complementary to standard practices of oil saturation mapping and of special value when analyzing the results of multiple realizations and/or development scenarios.
This paper discusses the use of a novel data-driven method for automated facies classification and characterization of carbonate reservoirs. The approach makes an extensive use of wireline and while drilling electrical borehole image logs and provides a direct and fast recognition of the main geological features at multi-scale level, together with secondary porosity estimation. This embodies an unbiased and valuable key-driver for rock typing, dynamic behavior understanding and reservoir modeling purposes in these puzzling scenarios.
The implemented methodology takes advantage of a non-conventional approach to the analysis and interpretation of image logs, based upon image processing and automatic classification techniques applied in a structural and petrophysical framework. In particular, the Multi-Resolution Graph-based Clustering (MRGC) algorithm that is able to automatically shed light on the significant patterns hidden in a given image log dataset. This allows the system to perform an objective multi-well analysis within a time-efficient template. A further characterization of the facies can be established by means of the Watershed Transform (WT) approach, based on digital image segmentation processes and which is mainly aimed at quantitative porosity partition (primary and secondary).
The added value from this data-driven image log analysis is demonstrated through selected case studies coming from vertical and sub-horizontal wells in carbonate reservoirs characterized by high heterogeneity. First, the MRGC has been carried out in order to obtain an alternative log-facies classification with an inherent textural meaning. Next, the WT-based algorithm provided a robust quantification of the secondary porosity contribution to total porosity, in terms of connected vugs, isolated vugs, fractures and matrix contribution rates. Finally, image log-facies classification and quantitative porosity partition have been integrated with production logs and pressure transient analyses to reconcile the obtained carbonate rock types with the effective fluid flows and the associated dynamic behavior at well scale.
The presented novel methodology is deemed able to perform an automatic, objective and advanced interpretation of field-scale image log datasets, avoiding time-consuming conventional processes and inefficient standard analyses when the number of wells to be handled is large and/or in harsh circumstances. Moreover, secondary porosity can be proficiently identified, evaluated and also characterized from the dynamic standpoint, hence representing a valuable information for any 3D reservoir models.
In this paper, the approach to multivariate static and dynamic modeling is considered on the example of an offshore field discovered in 2017. Based on the limited volume of information, the quantitative and qualitative description of uncertainties included further in the 3D modeling is made. This model is proposed to be used as a tool for prompt decision making when implementing a fast-track project with limited time between exploration and pre-FEED stages.
Gasser-Dorado, Julien (IFP Energies nouvelles) | Ayache, Simon Victor (IFP Energies nouvelles) | Lamoureux-Var, Violaine (IFP Energies nouvelles) | Preux, Christophe (IFP Energies nouvelles) | Michel, Pauline (IFP Energies nouvelles)
SAGD is commonly used as a thermal EOR method to produce heavy oil. However it suffers from the production of acid gases formed by aquathermolysis chemical reactions that occur between the steam, the sulfur-rich oil and the mineral matrix. The objectives of this paper are to take advantage of a comprehensive chemical model coupled to compositional thermal reservoir simulations to predict and understand the H2S production variation at surface according to the type of reservoir.
Thermal reservoir simulations coupled to both a SARA based 10-component / 5-reaction chemical model fully calibrated against laboratory data and a compositional PVT are used to simulate SAGD processes on heavy oil fields in Athabasca, Canada. Numerical results are then analyzed to provide a comprehensive analysis of the mechanisms leading to in-situ H2S generation and its production at wellheads based on compositional thermal simulations coupled to a fully laboratory calibrated SARA-based chemical model. Composition of the pre-steam, post-steam and produced oil are compared to understand the effect of the aquathermolysis reactions. The impact of heterogeneities on H2S production both in-situ and at surface can also be observed and explained, especially the variations in vertical permeability. Then simple reservoir models with two facies are used to further understand the impact of heterogeneities on H2S production at surface. Overall heterogeneous cases show important changes in the temperature distribution, fluid flows, reactions kinetics and steam chamber shape that lead to H2S production variations at surface. This detailed description of the involved mechanisms in acid gases production will allow operators to better forecast their H2S risks according to their reservoir properties.
Glover, Paul W. J. (University of Leeds) | Lorinczi, Piroska (University of Leeds) | Al-Zainaldin, Saud (University of Leeds) | Al-Ramadhan, Hassan (University of Leeds) | Sinan, Saddam (University of Leeds) | Daniel, George (University of Leeds)
New reservoirs are increasingly more heterogeneous and more anisotropic. Unfortunately, conventional reservoir modelling has a resolution of only about 50 m, which means it cannot be used to model heterogeneous and anisotropic reservoirs effectively when such reservoirs exhibit significant inter-well variability at scales less than 50 m. This paper describes a new fractal approach to the modelling and simulation of heterogeneous and anisotropic reservoirs. This approach includes data at all scales such that it can represent the heterogeneity of the reservoir correctly at each scale.
Three-dimensional Advanced Fractal Reservoir Models (AFRMs) can be generated easily with the appropriate code. This paper will show: (i) how 3D AFRMs can be generated and normalised to represent key petrophysical parameters, (ii) how these models can be used to calculate permeability, synthetic poro-perm cross-plots, water saturation maps and relative permeability curves, (iii) the effect of altering controlled heterogeneity and anisotropy of generic models on fluid production parameters, and (iv) how AFRMs which have been conditioned to represent real reservoirs provide a much better simulated production parameters than the current best technology.
Results of generic modelling and simulation with AFRMs show how total hydrocarbon production, hydrocarbon production rate, water cut and the time to water breakthrough all depend strongly both on heterogeneity and anisotropy. The results also show that in heterogeneous reservoirs, the best production data is obtained from placing both injectors and producers in the most permeable areas of the reservoir – a result which is at variance with common practice. Modelling with different degrees and directions of anisotropy shows how critical hydrocarbon production data depends on the direction of the anisotropy, and how that changes over the lifetime of the reservoir.
We have developed a method of fractal interpolation to condition AFRMs to real reservoirs across a wide scale range. Comparison of the hydrocarbon production characteristics of such an approach to a conventional krigging shows a remarkable improvement in the modelling of hydrocarbon production when AFRMs are used; with AFRMs in moderate and high heterogeneity reservoirs returning values always within 5% of the reference case, while the conventional approach often resulted in systematic underestimations of production rate by over 70%.
Both the computation of classical statistical measures (e.g., mean, mode, median, variance, standard deviation, and skewness), and graphic data representation (e.g., histograms and scatter plots) commonly are used to understand the nature of data sets in a scientific investigation--including a reservoir study. A distinguishing characteristic of earth-science data sets (e.g., for petroleum reservoirs), though, is that they contain spatial information, which classical statistical descriptive methods cannot adequately describe. Spatial aspects of the data sets, such as the degree of continuity--or conversely, heterogeneity--and directionality are very important in developing a reservoir model. Analysis of spatially rich data is within the domain of geostatistics (spatial statistics), but a foundation in classical statistics and probability is prerequisite to understanding geostatistical concepts. Sampling also has proved invaluable in thousands of studies, but it, too, can lead to statistical insufficiencies and biases.
Gathering elemental data from rock material using X-ray florescence (XRF)-techniques is a relatively common practice in the Delaware Basin. As such, operators often amass very large datasets comprising thousands of sample points that superficially appear ideal for “big data” projects. However, varying sampling strategies between core and cuttings but also over the lifetime of an analytical program means that not all data can be directly or meaningfully compared.
In this paper we discuss the pros and cons of different sampling approaches when attempting to resolve the questions in the following areas: (a) regional or field wide correlation, (b) characterization of landing zones and (c) characterization of depositional environments. Additionally, we will also show how data can be successfully integrated from high resolution core analysis (2-12 inch vertical resolution) to low resolution (30ft-50ft) vertical cuttings.
Typically, XRF datasets from cores are gathered at a high resolution over a relatively thin interval to provide detailed facies and depositional environment information. However, as these datasets can show such small-scale variability, it often becomes difficult to integrate this data into more regional and broader scale findings. Conversely, cuttings can be sampled from a far higher number of wells and as such cover a much wider stratigraphic and geographic area, with sample density controlled by sample collection. Datasets gathered from cuttings are commonly collected for either more regional characterization or for landing zone identification. Due to the composite nature of cuttings, however, high resolution facies variability in sequences can be lost at a 20 or 30ft interval, making it tough to meaningfully define landing zones and integrate data from lateral wells using vertical cuttings data alone.
Using a series of Delaware Basin case studies, we firstly show how relative changes in elements and element ratios, from widely spaced cuttings material in vertical wells, are used to provide robust regional correlation frameworks that complement and refine stratigraphic tops picked from wireline log data. We will also highlight some of the difficulties in tying vertical cutting data with high resolution core data and strategies to resolve these issues. Finally, we will show how data acquired from core can provide detailed insight into landing zone characterization and insights into the rapidly changing depositional environments of the Delaware Basin, through Wolfcamp and Bone Spring time. This type of analysis can be easily integrated with data from lateral wells in order to understand and ultimately predict the type of lateral facies variation expected from different landing zones.
By systematically working through XRF datasets at different sampling resolutions we are able to utilize and integrate these datasets more efficiently. Ultimately, we can start to understand the optimal sampling resolution needed to solve different characterization or stratigraphic problems in different Delaware Basin formations and design more focused and “fit for purpose” analytical programs in the future.
Tian, Jianwei (The University of Western Australia) | Liu, Jishan (The University of Western Australia) | Elsworth, Derek (The Pennsylvania State University) | Leong, Yee-Kwong (The University of Western Australia) | Li, Wai (The University of Western Australia) | Zeng, Jie (The University of Western Australia)
Heterogeneous pore structure is critically important for unconventional gas recovery. In this paper, a dynamic fractal permeability model is proposed to investigate the interplay between heterogeneous pore structures and gas transport for coal seam gas reservoir. In this model, pore diameter and fractal dimension of pore size distribution are dynamically changing as a result of the variation of effective stress. Besides, based on fractal approach, a new Klinkenberg coefficient that dynamically changes with pore pressure is employed to incorporate the non-Darcy effect. This dynamic permeability model is applied to couple Multiphysics in coal seam gas recovery process. The impacts of these fractal parameters on permeability evolution are explored through a benchmark reservoir simulation. The numerical results exhibit good agreements with experimental data. The simulation results indicate that: (1) the dynamic permeability model matches better with experimental data than other homogeneous models, especially in low-pressure stage; (2) reservoir with larger initial fractal dimension is more sensitive to pressure depletion; (3) fractal dimension would change more dramatically when initial porosity is relatively smaller; (4) Klinkenberg coefficient increases with the decreasing of reservoir pore pressure during gas depletion. In summary, the dynamic permeability model predicts permeability evolution well in gas production process and provide some fundamental insights into the implications of reservoir heterogeneity on gas transport in reservoir simulation.
With the natural gas depletion, there is an increasing need for the exploration of unconventional natural gas, unconventional gas is typically regarded as a substitute that can relieve energy supply shortage. Permeability is the dominant factor that controls unconventional natural gas production. Therefore, it is significant to understand the mechanisms of permeability evolution and the associated influential factors. Notably, coal reservoir exhibits multi-scale heterogeneity, and pore size spans from micrometer to nanometer, which affects gas transport and storage capability substantially.
The heterogeneous pore structure of coal reservoir is characterized by multiscale pore size distribution (PSD) and the tortuous flow channel. Knudsen number ( Kn ) is defined as the ratio between the molecular free path and characteristic length, which is usually applied to describe flow regimes. The gas flow regimes include viscous flow ( Kn < 0.001 ), slip flow ( 0.001 < Kn 0.1 ), transitional flow ( 0.1 < Kn < 10 ) and free molecular flow ( Kn > 10 ). According to the definition of Knudsen number, pore size distribution determines the flow regimes in micropores when pore pressure remains constant. Therefore, the pore structure of coal has a significant impact on the apparent permeability of coal matrix. Different distribution functions have been employed to study the effect of PSD on apparent permeability, demonstrating that permeability is highly sensitive to the variation of the distribution function (Tian et al. 2017, Civan 2002). When the proportion of micropores is larger, the specific surface will increase, which will provide much more adsorption volume for coal seam gas (Tian et al. 2017). The original gas in place (OGIP) and corresponding sorption-induced swelling can be influenced substantially. For coal seam at different depths, coal swells or contracts greatly depend on PSD (Yang et al. 2010). Except for porosity, tortuosity of pore structure is an essential parameter for permeability prediction, which reflects the ratio between actual flow length and characteristic length of coal sample. According to the Kozeny-Carman model, there is a negative correlation between permeability and tortuosity(Walsh and Brace 1984). The theoretical investigation indicates that large tortuosity can increase the resistance of gas transport (Wang et al. 2017).
The Devonian-Mississippian STACK/SCOOP Play of the Oklahoma Anadarko Basin is a complex assemblage of tight carbonate and siliciclastic strata and an important oil and gas province. In the last decade, prolific drilling has demonstrated significant heterogeneity in the composition of oils produced from STACK/SCOOP reservoirs. This study discusses possible geoscientific explanations for the heterogeneity observed in produced oils and describes how source, maturation, and migration affect their composition.
Geochemical data from 136 produced oils across 12 counties from 4 producing reservoirs is reviewed. Calculated thermal maturity (Rc%) from alkylated polyaromatic compounds shows excellent agreement with oil thermal maturity increasing with increased depth. Oils produced from overpressured reservoirs exhibit a strong relationship between Rc% and Gas-Oil Ratio (GOR), while normal- to underpressured reservoirs exhibit GORs up to an order of magnitude higher at similar Rc%. Light hydrocarbons show that paraffinicity varies starkly with producing reservoir, suggesting compositional fractionation from diffusive migration through tight and argillaceous strata. Conversely, aromaticity varies geographically by Play Region, indicative of changing depositional environments and organic input across the basin. Isoprenoid and sesquiterpane biomarkers indicate all oils are generated by Type II or Type II/III mixed organic matter, but Springer Group reservoirs are charged by a highly argillaceous, non-Woodford source.
The Anadarko Basin is the deepest sedimentary basin in the cratonic interior of the North America with as much as 40,000 feet of Paleozoic sediments (Johnson, 1989). The Anadarko is an asymmetric basin with the deepest sediments bound against the Amarillo-Wichita Uplift to the southwest. The basin is elongated along its west-northwest axis and bound by the Nemaha Ridge to the east and the Anadarko shelf to the west and north.
In the last decade, drilling of Devonian-Mississippian strata along the margins of the basin have delineated one the continent's most successful petroleum resource plays. These areas are colloquially referred to as the