Permeability values of rocks range over many factors of 10; therefore, permeability is plotted on a logarithmic scale. Values commonly encountered in petroleum reservoirs range from a fraction of a millidarcy to several darcies. This page discusses factors affecting permeability associated with different rock types. The log10(k)-Φ plot of Fig.1 shows four data sets from sands and sandstones, illustrating the reduction in permeability and porosity that occurs as pore dimensions are reduced with compaction and alteration of minerals (diagenesis). Porosity is reduced from a maximum of 52% in newly deposited sandstones to as low as 1% in consolidated sandstones.
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.
The goal of the net-pay calculations is to eliminate nonproductive rock intervals and, from these calculations at the various wellbores, provide a solid basis for a quality 3D reservoir description and quantitative hydrocarbons-in-place and flow calculations. The determination of net pay is a required input to calculate the hydrocarbon pore feet, FHCP, at a wellbore and its input to the overall reservoir original oil in place (OOIP) or original gas in place (OGIP) calculations. The total FHCP at a well is the point-by-point summation over the reservoir interval with Eq. 1. The top and base of the reservoir interval are defined by geologists on the basis of core descriptions and log characteristics. In the FHCP calculation, net pay, hni, at each data point has a value of either 1 (pay) or 0 (nonpay).
The amount of trapped oil in hydrocarbon rich shale reservoirs recoverable through Enhanced Oil Recovery methods such as low salinity water flooding has been an ongoing investigation in the oil and gas industry. Reservoir shales typically have relatively lower amounts of swelling clays and in theory, can be exposed to a higher chemical potential difference between the native and injected fluid salinity before detrimental permeability reduction is experienced through the volumetric expansion of swelling clays. This fluid flux into the pore spaces of the rock matrix acting as a semi permeable membrane is significant enough to promote additional recovery from the extremely low permeability rock. The main goal of this paper is to determine how osmosis pressure build up within the matrix affects geomechanical behavior and hydrocarbon fluid flow. In this study we investigate Pierre shale samples with trace amount of organic content and high clay content as an initial step to fully understanding how the presence of organic content affects the membrane efficiency for EOR applications in shales using low salinity fluid injection. The same concept is also valid when slickwater is utilized as fracturing fluid as majority of the shale reservoirs contain very high salinity native reservoir fluid that will create large salinity contrast to the injected slickwater salinity.
The organic-rich reservoir shales typically have a TOC content of approximately 5 wt% or higher with TOC occupying part of the bulk matrix otherwise to be filled up by clays and other minerals. With less clay within the rock structure, the amount of associated clay swelling arising from rock fluid interaction will be limited. The overall drive of water into the matrix brings added stress to the pore fluid known as osmotic pressure acting on the matrix that also creates an imbalance in the stress state. The native formation fluid with salinity of 60,000 ppm NaCl has been used while 1,000 ppm NaCl brine is utilized to simulate the low salinity injection fluid under triaxial stress conditions in this phase of the study reported here. A strong correlation is obtained between the osmotic efficiency and effective stress exerted on the shale formation. The triaxial tests conducted in pursuit of simulating stress alteration under the osmotic pressure conditions and elevated pore pressure penetration tests indicated that the occurrence of swelling directly impact the formation permeability. These structural changes observed in our experimental results are comparable to field case studies.
Anderson, Iain (Heriot-Watt University) | Ma, Jingsheng (Heriot-Watt University) | Wu, Xiaoyang (British Geological Survey) | Stow, Dorrik (Heriot-Watt University) | Underhill, John R. (Heriot-Watt University)
This work forms part of a study addressing the multi-scale heterogeneous and anisotropic rock properties of the Lower Carboniferous (Mississippian) Bowland Shale; the UK's most prospective shale-gas play. The specific focus of this work is to determine the geomechanical variability within the Preese Hall exploration well and, following a consideration of structural features in the basin, to consider the optimal position of productive zones for hydraulic fracturing. Positioning long-reach horizontal wells is key to the economic extraction of gas, but their placement requires an accurate understanding of the local geology, stress regime and structure. This is of importance in the case of the Bowland Shale because of several syn- and post-depositional tectonic events that have resulted in multi-scale and anisotropic variations in rock properties. Seismic, well and core data from the UK's first dedicated shale-gas exploration programme in northwest England have all been utilized for this study. Our workflow involves; (1) summarizing the structural elements of the Bowland Basin and framing the challenges these may pose to shale-gas drilling; (2) making mineralogical and textural-based observations using cores and wireline logs to generate mineralogy logs and then to calculate a mineral-based brittleness index along the well; (3) developing a geomechanical model using slowness logs to determine the breakdown stress along the well; (4) placing horizontal wells guided by the mineral-based brittleness index and breakdown stress. Our interpretations demonstrate that the study area is affected by the buried extension of the Ribblesdale Fold Belt that causes structural complexity that may restrict whether long-reaching horizontal wells can be confidently drilled. However, given the thickness of the Bowland Shale, a strategy of production by multiple, stacked lateral wells has been proposed. The mineralogical and geomechanical modelling presented herein suggests that several sites retain favorable properties for hydraulic fracturing. Two landing zones within the Upper Bowland Shale alone are suggested based on this work, but further investigation is required to assess the impact of small-scale elastic property variations in the shale to assess potential for well interference and optimizing well placement.
Almost simultaneously, advances were made in understanding both the processes within the source rock organic matter that accompany the generation and expulsion of hydrocarbons and in the acquisition, processing, and quantitative interpretation of 3D seismic data. In particular, as organic matter in shales in unconventional plays generates and expels hydrocarbons, porosity is formed in the organic matter and the organic matter becomes more dense and more brittle. As these changes are occurring at a micro-scale, extraction of hundreds of different attributes from a well-imaged 3D seismic volume has made it possible to observe changes at a macro-scale in seismic lines and horizons within that volume. Seismic attributes derived from pre-stack inversions yielding rock mechanical properties from shear (Vs) and compressional (Vp) velocities and density, when calibrated with well log and/or core measurements, can be combined to calculate TOC, pore pressure, rigidity, and compressibility because these properties cause fundamental changes in how seismic waves travel through the rock.
Equally important, the escalation in computing power via methods such as machine learning, neural networks, and multivariate statistics has made it possible to interpret large amounts of data. All of these innovations have contributed to better identification of sweet spots within unconventional plays. Such sweet spots include areas with elevated TOC values, enhanced porosity, and zones that can be targeted for fracking.
One of the primary advantages of seismic data is that it provides information in those areas in between control points/wells. This information in turn helps operators to better select targets for wells and for landing zones. Carefully tied 3D seismic inversion and integration with petrophysical and rock data further allow for detailed characterization of unconventional reservoirs. The enhanced ability to identify the best potential drilling targets has significant economic implications in terms of risk reduction and improved chances to find economic prospects.
While 3D seismic data is being used routinely by numerous companies to predict the mechanical properties, density, and associated TOC of many formations, there is yet to be a direct link made between TOC loss, kerogen conversion, and the associated changes in rock properties. This work documents the importance of TOC loss during maturation and its effects on rock properties like porosity, density, brittleness, and how those advances coupled with the advances in quantitative interpretation of 3D seismic data are enabling the unconventional operators to predict location, thickness, landing zone, and sweet spots with appropriately acquired, processed, and interpreted 3D seismic. Meticulously calibrated 3D seismic inversion and integration with petrophysical and rock data permit detailed reservoir characterization of unconventional reservoirs.
Updated methods for the back calculation of original TOC have been developed using well logs, rock measurements, and 3D basin modeling to assist in locating and developing unconventional reservoirs. In addition, petrophysical measurements that reflect TOC and porosity and are related to fundamental properties controlling the seismic response can be extracted from the seismic reflection data. In turn, seismic attributes derived from pre-stack inversions yielding rock mechanical properties from shear (Vs) and compressional (Vp) velocities and density, when calibrated with well log and/or core measurements, can be combined to estimate TOC, pore pressure, rigidity, and compressibility because these properties cause basic modifications in how seismic waves travel through the rock.
This study shows advancements in studies of: 1) TOC loss with increased thermal maturation, 2) how this loss affects the development of organic porosity, 3) how kerogen becomes denser, harder, and more brittle with increasing maturity, and 4) how recent developments in quantitative interpretation workflows for 3D seismic data facilitate estimation of TOC and determination of rock mechanical properties from shear (Vs) and compressional (Vp) velocities and density. Further integration of geochemical, geomechanical, and geophysical technologies and measurements will provide improved estimates of present-day TOC that can in turn be extended to relative maturity and percent conversion.
Examples provided in this work illustrate prediction of present-day TOC, porosity, density, and mechanical properties extracted from high fidelity pre-stack inversion. Pre-stack inversion along with machine learning can be used to predict rock properties such as porosity, TOC, organic matter quality, rigidity, and pressure and to correlate those properties back to well productivity for improved execution. Relating present TOC estimated from seismic to TOC loss and kerogen property changes with increasing maturity is possible by combining the results of these technologies.
Though analysis and inversion of painstakingly acquired modern 3D seismic data is capable of estimating porosity, TOC, matrix strength, and pore pressure, the latest work on rock property changes as hydrocarbons mature and are expelled isn't typically addressed in most studies. Increasing communication between disciplines might improve estimation of these properties and extend the capability to assess the extent of TOC loss during maturation and the porosity increases that accompany it. This ability is especially important in the intra-well regions where the potential of 3D seismic to extend data between control points enables better reserve estimates and high grading of acreage. After carefully calibrating a quantitative 3D seismic interpretation with a 3D basin modeling analysis of the source rock potential and maturity, an operator is better prepared to high grade acreage and attain the most economic development of unconventional resources.
The escalation in computing power means there are hundreds of different attributes that can be extracted or calculated from a well-imaged 3D seismic volume. Using quantitative calibration of fundamental geochemical measurements such as TOC, pyrolysis, and petrographic measurements of vitrinite reflectance that yield the quantity, quality, and maturity of organic matter in combination with well log and seismic data creates a model for identifying sweet spots and the areas in the target formation that exhibit high TOC, high porosity, and elevated brittleness. Further integration and calibration of changes occurring at the micro-level in organic matter in unconventional plays with their impact on the signatures of data at the macro-level can provide information on the types of hydrocarbons most likely to be found in these sweet spots as well as identifying which zone(s) in the target formation are most likely to be amenable to fracking. Used together, the advances outlined here result in a technological evolution that could have a substantial impact on: 1) the approach to and 2) the economics of the exploration and production of unconventional plays.
Shoemaker, Michael (Callon Petroleum Company) | Hawkins, James (Callon Petroleum Company) | Becher, John (Callon Petroleum Company) | Gonzales, Veronica (Callon Petroleum Company) | Mukherjee, Sandeep (Callon Petroleum Company) | Garmeh, Reza (Callon Petroleum Company) | Kuntz, David (Callon Petroleum Company)
E&P companies in the Permian Basin typically implement basin-wide development strategies that involve cookie-cutter type methods that use multi-well pads with identical geometric stage and cluster spacing. Such development strategies however fail to recognize and account for subsurface stress heterogeneity, and thus assume similar geomechanical properties that are homogeneous and isotropic which may cause well-to-well interference or “frac hits”, particularly near “parent” wells as fields continue to mature.
Minimum horizontal stress (Sh) is the leading parameter that controls hydraulic fracture stimulation, but is next to impossible to measure quantitatively, especially far field and in 3D space. In-situ stress differences from fluid depletion, combined with stratigraphy and subsequent mineralogy contrasts, control fracture containment vertically and laterally which define fracture propagation and complexity. Far field preference of virgin rock towards brittle vs ductile deformation is governed by mineralogy which defines the elastic moduli or geomechanical behavior of the rock. When integrated with pore pressure and overburden stress, the elastic rock properties are characterized by the Mechanical Earth Model (or MEM) which defines key inputs for calculating Sh using the uniaxial Ben Eaton stress equation. However, implementing this model historically produces incorrect calculated stress, when compared to field measured stress, due to an assumed homogeneous and isotropic subsurface.
Parameterization of fracture geometry models for well spacing, frac hit mitigation, and engineered treatment design in shale (or mudrock) requires an anisotropic in-situ stress measurement that accurately captures subsurface stress states. A method herein is proposed that achieves this using a modified version of the anisotropic Ben Eaton stress equation. The method calculates minimum horizontal stress by substitution of AVO seismic inversion volumes directly into the stress equation, replacing the bound Poisson's ratio term with an equivalent anisotropic corrected Closure Stress Scalar (CSS) defined in terms Lamé elastic parameters, specifically lambda (λ) or incompressibility and mu (μ) for shear rigidity. The CSS volume is corrected for anisotropy using static triaxial core, and is calibrated to multi domain data types including petrophysics, rock physics, completion engineering, and reservoir engineering (DFIT) measurements.
Successful application of said method in the Delaware and Midland sub-basins (of the greater Permian Basin) is shown. Anisotropic minimum horizontal stress (Sh) volumes from 3D seismic defined at 1 ft. vertical log resolution were interpreted quantitatively regionally, particularly as a prevention tool near parent wells prone to frac-hits. Moreover, the method provides an anisotropic measurement of in-situ stress variability (or stress differential) to qualitatively model 3D fracture geometries for engineered treatment optimization. Current stress modeling methods rely on the propagation of geomechanical properties from well control, which do not necessarily represent rock properties and stress states at the area of interest.
Pore pressure prediction plays a critical role in the ability to predict areas of high overpressure and fracture behavior for the exploitation of unconventional plays, which are both correlated with production. Shales in these plays have variable clay content and complex multi-mineral fractions that require a detailed petrophysical assessment reinforced with rock physics modelling as needed. For example, changes in total organic content have a similar elastic response to changes in porosity. Therefore, any pressure-stress property model for unconventional plays must be supported by petrophysically conditioned elastic logs and accurate multi-mineral volume sets calibrated to core data.
A supervised deep neural network approach is introduced as an alternative innovative tool for petrophysical, pore pressure and geomechanics analysis enabling the use of all the previously collected and interpreted data to devise solutions which simultaneously integrate wide ranging well bore and wireline logs. We implement three neural networks, all with similar structure, as each of these networks had a different objective and the outputs from one were the inputs for the other.
The first network was trained to predict petrophysical volume logs (shale, sand, dolomite, calcite, kerogen and also porosity) simultaneously from compressional velocity (Vp), Gamma ray, density (rho), resistivity and Neutron logs. The second neural network, cascaded from the first, was then designed to match the manually predicted pore pressure. The inputs were Vp and shear velocity (Vs), Rho, resistivity, Neutron logs as well as the results of the first network. The third network focused on predicting various properties of interest, in this case pore pressure, minimum horizontal stress (Shmin), maximum horizontal stress (SHmax), and volume of kerogen, based on only Vp, Vs, and Rho logs which is an example building a neural network capable of predicting key rock properties directly from seismic inversion results to produce meaningful 3D interpretations.
The volumetric pore pressure model was also positively correlated to cumulative production values from blind long horizontal wells. The results show a promising outlook for the application of deep learning in integrated studies such as those shown in this paper.