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Abstract Image-based characterization of rock fabric is critical for understanding recovery mechanisms in shale formations due to the significant multiscale nature of shale source rocks. Nanoscale imaging is particularly important for characterizing pore-scale structure of shales. Nanoimaging techniques, however, have a tradeoff between high-resolution/high-contrast sample-destructive imaging modalities and low-contrast/low-resolution sample-preserving modalities. Furthermore, acquisition of nanoscale images is often time-consuming, expensive, and requires signficant levels of expertise, resulting in small image datasets that do not allow for accurate quantification of petrophysical or morphological properties. In this work, we introduce methods for overcoming these challenges in image-based characterization of the fabric of shale source rocks using deep learning models. We present a multimodal/multiscale imaging and characterization workflow for enhancing non-destructive microscopy images of shale. We develop training methods for predicting 3D image volumes from 2D training data and simulate flow through the predicted shale volumes. We then present a novel method for synthesizing porous media images using generative flow models. We apply this method to several datasets, including grayscale and multimodal 3D image volume generation from 2D training images. Results from this work show that the proposed image reconstruction and generation approaches produce realistic pore-scale 3D volumes of shale source rocks even when only 2D image data is available. The models proposed here enable new capabilities for non-destructive imaging of source rocks and we hope will improve our ability to characterize pore-scale properties and phenomena in shales using image data.
Summary Stimulated reservoir volume (SRV) is a prime factor controlling well performance in unconventional shale plays. In general, SRV describes the extent of connected conductive fracture networks within the formation. Being a pre-existing weak interface, natural fractures (NFs) are the preferred failure paths. Therefore, the interaction of hydraulic fractures (HFs) and NFs is fundamental to fracture growth in a formation. Field observations of induced fracture systems have suggested complex failure zones occurring in the vicinity of HFs, which makes characterizing the SRV a significant challenge. Thus, this work uses a broad range of subsurface conditions to investigate the near-tip processes and to rank their influences on HF-NF interaction. In this study, a 2D analytical workflow is presented that delineates the potential slip zone (PSZ) induced by a HF. The explicit description of failure modes in the near-tip region explains possible mechanisms of fracture complexity observed in the field. The parametric analysis shows varying influences of HF-NF relative angle, stress state, net pressure, frictional coefficient, and HF length to the NF slip. This work analytically proves that an NF at a 30 ± 5° relative angle to an HF has the highest potential to be reactivated, which dominantly depends on the frictional coefficient of the interface. The spatial extension of the PSZ normal to the HF converges as the fracture propagates away and exhibits asymmetry depending on the relative angle. Then a machine-learning (ML) model [random forest (RF) regression] is built to replicate the physics-based model and statistically investigate parametric influences on NF slips. The ML model finds statistical significance of the predicting features in the order of relative angle between HF and NF, fracture gradient, frictional coefficient of the NF, overpressure index, stress differential, formation depth, and net pressure. The ML result is compared with sensitivity analysis and provides a new perspective on HF-NF interaction using statistical measures. The importance of formation depth on HF-NF interaction is stressed in both the physics-based and data-driven models, thus providing insight for field development of stacked resource plays. The proposed concept of PSZ can be used to measure and compare the intensity of HF-NF interactions at various geological settings.
Summary Shale, which has pores as small as 10 nm, is economically viable for hydrocarbon recovery when it is fractured. Although the fracture toughness dictates the required energy for the improvement, the existing techniques are not suitable for characterization at scales smaller than 1 cm. Developing practical methods for characterization is crucial because fractures can contribute to an accessible pore volume at different scales. This study proposes a conceptual model to characterize the anisotropic fracture toughness of shale using nanoindentations on a sub-1-cm scale. The conceptual model reveals the complexities of characterizing shales and explains why induced fractures differ from those observed in more-homogeneous media, such as fused silica. Samples from the Wolfcamp Formation were tested using Berkovich and cube-corner tips, and the interpreted fracture toughness values are promising. The conceptual model is the first application of the effective-medium theory for fracture toughness characterization using nanoindentation. In addition, it can quantify fracture toughness variations when using small samples, such as drill cuttings. Introduction Shale is a sedimentary rock containing clay minerals and silt-sized particles (Blatt and Tracy 1996) with a pore size of smaller than 100 nm in its matrix, which results in ultralow permeability. Shale gas was first extracted in 1821 (Hill et al. 2004) and has recently become economically viable because of hydraulic fracturing. This has made the US a significant fossil fuel producer. Since the Stanolind Oil and Gas Corporation performed the first hydraulic fracturing using water-based muds in 1947 (King 2012), many stimulations have shown favorable results and an increased recovery rate.
Xu, Guoqing (Sinopec Research Institute of Petroleum Engineering (Corresponding author) | Han, Yujiao (email: firstname.lastname@example.org)) | Jiang, Yun (Sinopec Research Institute of Petroleum Engineering) | Shi, Yang (Research Institute of Petroleum Exploration & Development, PetroChina (Corresponding author) | Wang, Mingxian (email: email@example.com)) | Zeng, XingHang (Research Institute of Petroleum Exploration & Development, PetroChina (Corresponding author)
Summary Spontaneous imbibition (SI) is regarded as an effective method to improve the oil recovery in a tight sandstone reservoir, which leads to a significant change in fracturing design and flowback treatment. However, a longtime shut-in period would aggravate the retention of fracturing fluid, which is in contradiction with high production in the field. It is imperative to understand how SI works during shut-in time, so as to maximize the effect of imbibition in oil recovery enhancement. In this study, a series of experiments were conducted to simulate the status of residual oil saturation so that the inner mechanism of imbibition on oil recovery can be investigated. Low-field nuclear magnetic resonance (LF-NMR) was used to provide direct observation of phase changes in different pore sizes. The experimental results show a positive effect of imbibition on residual oil reduction. This phenomenon further elucidates the observations made during the well shut-in, soaking period, and low flowback efficiency. This study aims to understand the mechanism of SI behavior and help to improve the accuracy of production prediction.
Peng, Sheng (University of Texas at Austin (Corresponding author) | Shevchenko, Pavel (email: firstname.lastname@example.org)) | Periwal, Priyanka (Argonne National Laboratory) | Reed, Robert M. (University of Texas at Austin)
Summary Water-oil displacement is an important process that occurs in a shale matrix after hydraulic fracturing and in water-based enhanced oil recovery. Current understanding of this displacement process is limited because of the complicated pore structure and surface properties in shale. In this work, this process and its controlling factors are investigated through a comparative study of three shale samples that have different types of pore systems and wettability. An integrated method of imbibition and multiscale imaging was applied, and a modified oleic tracer that can better represent oil flow was used in imbibition testing and micro-computed tomography (CT) imaging. Scanning electron microscope (SEM) pore characterization was then performed under high magnification with guidance from the micro-CT images showing the changes caused by oil or water imbibition. New insights were obtained on the importance of both wettability and pore size effect on oil recovery and the distribution of residual oil after water-oil displacement. Connectivity of pores with different wettability is also discussed based on 3D analysis and SEM pore characterization. Collectively, these new findings improve the understanding of the complicated process of water-oil displacement and the role of influencing factors. Important implications for improved oil recovery strategy in shale are discussed for different types of reservoir rocks. The integrated imaging and imbibition technique provides a new path for further investigation of improved oil recovery in shale.
Smith, Christopher (Advanced Hydrocarbon Stratigraphy) | Pool, Susan (West Virginia Geological and Economic Survey) | Dinterman, Philip (West Virginia Geological and Economic Survey) | Moore, Jessica (West Virginia Geological and Economic Survey) | Vance, Timothy (West Virginia Geological and Economic Survey) | Smith, Timothy (Advanced Hydrocarbon Stratigraphy) | Gordon, Patrick (Advanced Hydrocarbon Stratigraphy) | Smith, Michael (Advanced Hydrocarbon Stratigraphy)
Abstract The distribution of liquid hydrocarbon (HC) resources in the Marcellus Formation throughout West Virginia (WV) is a matter of economic importance for the State of West Virginia and Marcellus operators. Herein, the West Virginia Geological and Economic Survey (WVGES) and Advanced Hydrocarbon Stratigraphy (AHS) have undertaken a project to map the composition and quantities of liquid gasoline range HCs present in drilling cuttings from counties in and neighboring the WV liquids fairway using Rock Volatiles Stratigraphy (RVStrat). Cuttings were analyzed from 12 wells, including air drilled wells, from Doddridge, Marshall, Ritchie, Tyler, Harrison, and Wetzel counties; spud dates range from 1953-2013. Insights into the geographical distribution of liquids quantities and compositions and the regional petroleum system were gained with a focus on the Devonian-aged shales, i.e. the upper and lower Marcellus Formation and the West River and Geneseo shale members of the Genesee Formation. Major results were identification of apparent thermal maturity trends embedded in the liquids composition across the basin where there is a trend of increasing paraffin (alkane) and decreasing naphthene (cycloalkane) content as a function of depth. A trend of decreasing size (number of carbon atoms) of the liquid molecules vs depth was observed in the West River, Geneseo, and upper Marcellus indicative of thermal maturity. The liquids distribution across the Marcellus fits within expectations from production data showing a trend of increasing content moving westward from northcentral WV towards the Ohio River; liquid saturations measured were likely ≤1% of the original subsurface saturation. The liquids content in the Marcellus shows an apparent declining exponential vs depth trend likely linked to the progression of catagenesis. An anomalous well that may have undergone a significant gas migration/expulsion event, resulting in less liquid content and a preferential depletion of the more volatile liquid HC species was identified. There is also a trend of increasing mechanical strength of the cuttings vs depth likely due to compaction; there are differences in mechanical strength as function of when the well was drilled, before or after 2009 (likely due to PDC [polycrystalline diamond compact drill bits); this was the only bias identified due to the age of the sample or mud system used. The value of being able to collect usable and meaningful geochemical data from air drilled wells where the cuttings are several decades old with minimal cuttings material by RVStrat should not be understated; it allows using samples that are typically considered unsuitable and offers unique opportunities for petroleum system assessments.
Abstract The Lewis Shale is a turbidite system encompassing sandstones, siltstones, and organic-rich shales, deposited during the last Cretaceous seaway transgression. It is informally subdivided into three members; a lower member (characterized by high clay and organic matter content), a middle member (a mixture of siltstones, shales, and sandstones), and an upper member or Dad sandstone member (with decreasing amounts of sandstone and greenish-grey shales) that can reach up to 2600 ft. in thickness (Almon, 2002). Its lithological characteristics vary depending upon its location in the Lewis depositional basin (eastern Greater Green River Basin). The present study is located in the Sweetwater and Carbon counties in Wyoming. Data includes three cores in the Great Divide Basin and one in the Wamsutter Arch provided by MorningStar Partners/Southland Royalty. Cores contain various lithologies, including shales, siltstones, and sandstones, representing the Lewis Shale's lithologic heterogeneity and complexity. Reservoir quality and lithology are intrinsically related. Therefore, high-resolution reservoir characterization must be performed to understand these different intervals and forecast some of the reservoir properties and possible challenges. Measured and sampled core data includes X-ray Fluorescence (XRF), X-ray diffraction (XRD), and Routine core analyses (RCA). Well-log data obtained from the Wyoming Conservation Commission (WOGCC) and donated by TGS were used to perform correlations, build maps of the different cored intervals, and evaluate its internal characteristics and reservoir quality. Core description and X-ray Fluorescence spectroscopy (XRF) analyses were performed every 0.5 ft. Samples for thin sections and X-ray diffraction (XRD) were taken in areas of interest based on lithology changes. Well-logs were correlated using the Gamma Ray (GR) signature for the cored and adjacent intervals. The objective of this work is to develop a high-resolution reservoir characterization. This analysis is crucial for understanding this play and decreasing uncertainty when planning new well placements. Although there have been several studies that identify the primary minerals and analyze the reservoir quality of some intervals of the Lewis Shale, none has been a high-resolution study combining XRF data, nor have any been oriented to horizontal drilling and unconventional reservoirs (Thyne et al., 2003; Pasternack, 2005, Sapardina, 2012). Correlations helped identify the heterogeneity and possible complications these turbiditic reservoirs can present, such as target pinch out and an increase in clay content that could cause swelling or instability in the wellbore and a possible loss of the well or the need to drill a sidetrack. Furthermore, this was also evidenced in the thin section, XRD, and XRF analyses showing mineralogical variations down to the inches scale, which is lower than the well-log resolution, making it challenging to identify. The overall composition of all the intervals is very consistent with high quartz content, followed by clay and carbonate contents. The presence of illite/smectite swelling clays can increase the wellbore problems and reduce porosity and permeability. According to Wang and Gale (2009), the brittleness of rock or fracability is controlled by several factors such as strength, lithology, texture, effective stress, temperature, fluid type diagenesis, and Total Organic Carbon (TOC). The brittleness index helps quantify some of these factors based on the mineral composition and diagenesis of the rock, without calculating Young's and Poisson moduli. According to Jarvie (2005), minerals that affect this index the most are quartz (the higher the quartz content, the higher the brittleness) and Carbonate, Clay, and TOC content (these decrease the brittleness index). Wang and Gale (2009) also included dolomite as a brittle mineral. However, the brittleness index was not calculated on these cores due to the absence of TOC measurements in some of them and the formula could not be applied. An approximation of how brittle a rock can be based on the formula's principle, where the higher the percentage of quartz and dolomite, the higher the brittleness index. In instances where calcite percentage is high, it can act as a brittle mineral. Dolomite is found as grains and cement, augmenting the rock's brittleness, but it can also decrease its porosity and permeability. Although, in general, the high quartz, calcite, dolomite, and plagioclase content in all the cored intervals makes them particularly brittle, thus facilitating hydraulic fracturing.
Abstract Electron tomography (ET) imaging provides nanometer-scale measurements of the structure of solid samples. ET, with an appropriate reconstruction workflow, provides significant insight into a variety of shale properties including structure, fluid transport and reaction. Despite the advantageous high resolution, the tomographic reconstruction of a three-dimensional (3D) structure from multiple 2D projections presents several challenges. In particular, because of the limited transmission of electrons through the sample, the signal to noise ratio remains small and the projection range is restricted to only a partial set of angles (±60° tilt angle). This missing wedge of angles results in tomographic artifacts that deteriorate the quality of the reconstructed volume, thereby compromising the accurate characterization of the nanometer-scale network of pores. To address these challenges, this study evaluates quantitatively the capabilities of ET. This work sits at the intersection of two disciplines: unconventional reservoir evaluation and computational imaging. It is the first of its kind to offer a comparison of the robustness of ET reconstruction methods applied to a shale thin section. Specifically, the properties of an organic pore network are compared over different reconstruction algorithms. First, Fourier techniques (Weighted Back-Projection, Filtered Back-Projection) and iterative techniques (Conjugate Gradient Least Squares, Simultaneous Iterative Reconstruction Technique) are used to reconstruct the structure of a Barnett shale sample. The pore network inside of and around a region of organic matter is visualized in 3D. Comparisons between the different reconstruction methods highlight the variability observed in regions with small pores. This variability is then quantified by comparing values of storage and transport metrics over the different methods. Finally, synthetic numerical phantoms and model samples are used to illustrate the limitations of the method and quantify the effect of artifacts. It is found that a strong agreement is observed for large pores over the different ET reconstruction workflows considered while a wider variability exists for small features. As a result, even though ET can provide robust estimates of the porosity of shale samples, the accurate determination of connectivity and transport metrics remains challenging due to missing wedge artifacts and noise limitations. The results suggest that application of ET to such samples benefits from artifact-reduction techniques. Experimental reduction in the missing wedge, and advanced reconstruction methods that fill-in the missing information, represent promising opportunities to characterize more accurately pore network properties in shale samples.
Abstract Rock volatiles stratigraphy (RVS) has been pioneered and developed over the last ten years to provide actionable information to oil and gas operators based on detailed geochemical analysis of volatile components present in geological samples. In this study, samples of the Mowry Shale from the Ainsworth 13-35 core (Bighorn Basin) and the Poison Spider No. 8 core (Wind River Basin) were characterized by RVS. Results are compared to standard bulk geochemical datasets with the goal of refining RVS interpretation in immature and early oil-window source rocks. The RVS technique applies vacuum extraction to freshly crushed core or cuttings samples to extract and provide quantitative or relative abundance information on hydrocarbons (HC), organic and inorganic acids, noble gases, air components, various sulfur compounds, and water. This includes aliquots extracted under two degrees of vacuum, 20 and 2 mbar, to obtain readily extracted and more tightly held compounds. Analytes are concentrated on liquid nitrogen cold traps (CT). The CT is then warmed, and analytes are released by sublimation point to a mass spectrometer for analysis. Non-condensable gases like methane and helium are analyzed prior to warming. Analysis at different pressures allows for calculation of relative permeability indices and evaluating environments where compounds reside. The RVS datasets demonstrate correlations to other bulk properties, including RVS-derived gas-oil ratios (GOR) and hydrogen index (HI) from programmed pyrolysis, with higher GOR values corresponding to lower HIs and vice versa. Higher volatile HC content was observed in intervals with higher total organic carbon content. The average distribution of C1-C5 compounds is also comparable between the two wells. A low water zone was observed by RVS at the contact between the upper part of the Mowry Shale and the informal Octh Louie sand, where a lateral was landed in the Ainsworth well. In core chips or rock bit cuttings, much of the original porosity remains intact, compared to polycrystalline diamond compact bit cuttings, and RVS water data from different extraction pressures relates to pore size and wettability. Water is extracted much more readily in the middle Mowry than the shallower shales and sands in the Ainsworth well, consistent with higher S3 responses and a more hydrophobic rock matrix. Correlations of RVS to well logs and core plug data suggest that the more thermally mature Mowry may have better permeability. RVS data provides information about the quality and type of resource present in the upper vs middle Mowry and their inorganic compositions. Based on HC compositional trends, the upper Mowry appears to have a much less dense resource than the Octh Louie and middle Mowry. The upper Mowry also appears to contain a greater ratio of HC gases vs liquids, greater aromatic content, and possibly fewer small molecule sulfur compounds. The nature of the water release from the rock provides relevant information for production and completions. Other non-HC species inform on biological activity and depositional environment; there is evidence of subsurface biological activity altering the organic matter.
Mehana, Mohamed (Los Alamos National Lab) | Santos, Javier E. (Los Alamos National Lab / University of Texas at Austin) | Neil, Chelsea (Los Alamos National Lab) | Sweeney, Matthew R. (Los Alamos National Lab) | Hyman, Jeffery (Los Alamos National Lab) | Karra, Satish (Los Alamos National Lab) | Xu, Hongwu (Los Alamos National Lab) | Kang, Qinjun (Los Alamos National Lab) | Carey, James William (Los Alamos National Lab) | Guthrie, George (Los Alamos National Lab) | Viswanathan, Hari (Los Alamos National Lab)
Abstract Hydrocarbon production from shale reservoirs is inherently inefficient and challenging since these are low permeability plays. In addition, there is a limited understanding of the fundamentals and the controlling mechanisms, further complicating how to optimize these plays. Herein, we summarize our past and current efforts to reveal the shale fundamentals and devise development strategies to enhance extraction efficiency with a minimal environmental footprint. Integrating these fundamentals with machine learning, we outline a pathway to improve the predictive power of our models, enhancing the forecast quality of production, thereby improving the economics of operations in unconventional reservoirs. For instance, we have developed science-informed workflows and platforms for optimizing pressure-drawdown at a site, which allow operators to make reservoir-management decisions that optimize recovery in consideration of future production. Recently, our work relies on the hybridization of physics-based prediction and machine learning, whereby accurate synthetic data (combined with available site data) can enable the application of machine learning methods for rapid forecasting and optimization. Consequently, the workflow and platform are readily extendable to operations at other sites, plays, and basins. Introduction Shale reservoirs have redefined the energy landscape of the world (Melikoglu, 2014). Shale reservoirs currently contribute to 70% of U.S. natural gas production and 60% of U.S. oil production (Perrin & Geary, 2019). However, these reservoirs possess unique characteristics that entail custom-designed development plans (Mehana & El-monier, 2016). Currently, we recover less than 10% of the hydrocarbons in place from these low permeability plays with the most efficient development plan (Alharthy et al., 2015). This low efficiency is also due to the limited understanding of shale characteristics' effects on the fluid properties and transport through porous media. Therefore, a fundamental understanding and quantification of these effects are required for devising better development plans to improve the current recovery factors (Middleton et al., 2017).