Polymers are used to viscosify the fluid. Crosslinkers are used to change the viscous fluid to a pseudoplastic fluid. Biocides are used to kill bacteria in the mix water. Buffers are used to control the pH of the fracture fluid. Surfactants are used to lower the surface tension. Fluid-loss additives are used to minimize fluid leakoff into the formation. Stabilizers are used to keep the fluid viscous at high temperature. Breakers are used to break the polymers and crosslink sites at low temperature.
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.
One of the major challenges associated with the exploitation of unconventional hydrocarbon resources is determining the optimal stimulation design. In this sense, it is necessary to understand how the parameters and variables involved in the completion process impact on production performance; the purpose is to act on such controllable variables and, consequently, maximize production and field development efficiency. Whereas physical driven tools frequently used in the oil industry are very helpful, they always imply a set of assumptions and simplifications regarding the system or phenomenon they try to model; they also require a large amount of unavailable or expensive data to calibrate them. Generally, different combinations of model parameters could explain well production behavior and for each of these solutions the way to optimize completion and development may be different.
Because of these drawbacks, and the big number of unconventional wells available, data-driven workflows have gained popularity in the last years. These models represent an excellent complement to physical driven tools in the attempt to optimize the completion and development strategy in shale plays. Several publications used both parametrical and non-parametrical models in the search of the Holy Grail: a statistical model capable of predicting how stimulation design affects productivity. The aim of this paper is to develop a novel methodology to understand the relation between formation parameters, completion design variables and production performance. An artificial neural network model (ANN) was chosen for this study.
Public production and stimulation data was merged with geological and petrophysical properties maps for almost 13.000 horizontal wells landed in Eagle Ford formation. A back propagation ANN algorithm was trained with this data-set and a cross-validation criterion was used for hyper-parameters optimization. Once the optimal model was selected, a bootstrap algorithm was run to assess for uncertainty in model prediction; these models were trained to determine which part of the input space presented enough data to get a clear signal and in which part the amount of data was not enough to differentiate signal from noise.
ANN models proved to be a fine method for this purpose obtaining R-Squared values between 0.5 and 0.7 for cross-validation sets. Significant relations were observed between production performance and lateral length, true vertical depth, porosity and fracture fluid intensity.
The methodology presented in this paper introduces a novel feature in comparison to previous publications regarding model uncertainty assessment. The coupling of the ANN model with the bootstrap re-sampling technique allowed to better understand which conclusions were statistically significant and which not, a fact that proved to be vital to correctly interpret results. It was demonstrated that such methodology is a good complement to physical-driven models in the aim to comprehend the relation between formation parameters, completion design variables and production performance.
A new approach that uses logs derived from wireline and surface drilling data to extract an interface proxy is presented and illustrated in the Montney. The derived interface proxy logs are propagated in the entire reservoir volume using artificial intelligence-based reservoir modeling. Blind wells confirm the ability to predict the interface proxy at any reservoir location. The derived interface proxy propagated in 3D was validated with moment tensor showing that the microseismic shear plane events occur mainly where the presence of the interfaces is the highest.
Using the derived interface proxy as an input, the Material Point Method (MPM) and Anisotropic Damage Mechanics (ADaM) are used to solve the geomechanical modeling of a hydraulic fracture propagating in a layered medium containing any type of interfaces including the weak interfaces. The geomechanical simulation confirms the major impact these weak interfaces could have on the fracture height growth.
The geomechanical analysis confirmed the importance of mapping in 3D the interfaces and modeling their effects in an accurate manner to better capture their effect on fracture height growth and the resulting proppant placement. The application of the new geomechanical workflow was illustrated on two Montney wells and was able to provide some explanation on their production differences that could be attributed to interfaces.
There is an ongoing paradigm shift in the processes and technologies employed in making field development decisions in unconventional reservoirs. Expensive trial and error exercises in multiple reservoirs have returned the verdict: there is no single prescribed treatment for a given reservoir, which always maximizes production and eliminates risk of frac hits and well interferences. In many situations, lateral growth of hydraulic fractures has been the major concern amongst operators, but as the economics of unconventional production shift, and the industry moves to more wine-racking and cube development plans, it has become abundantly clear that current hydraulic fracturing design software have multiple shortcomings such as not being able to fully account for natural fractures and predicting the subsequent frac-complexity as well as including the critical effects of weak interfaces. One of the consequences of this poor representation of the physics occurring during hydraulic fracturing of unconventional wells is the overprediction of hydraulic fracture heights. All commonly used industry frac design software are neither able to predict microseismicity to prove their ability to reproduce the observed frac complexity nor capable of including the effects of weak interfaces, or bedding and laminations (geologically speaking) on hydraulic fracture propagations in the vertical direction. Since microseismicity has been successfully predicted to capture the lateral stress gradients created by the natural fractures, the focus in this study is quantifying at any well the characteristics of the interfaces and their impact on the fracture height. Geomechanical logs derived from commonly available surface drilling data are used to capture zones of high interface potential and their characteristics. The resulting interface positions and their mechanical properties are input in a geomechanical simulator using the Material Point Method (MPM) to simulate the effect of the weak interfaces on hydraulic fracture height growth. These simulations provide the necessary information required by frac design software that now can incorporate not only the lateral stress gradients created by the natural fractures but also the vertical complex effects created by the weak interfaces. The results of this fast-practical decoupled workflow are a better estimate of the spacing needed for wine-rack systems and more realistic fracture geometries inputs to fluid flow models which can provide realistic geometries of depletion profiles affecting well interference potentials driven by production.
Travers, Patrick (Dolan Integration Group) | Burke, Ben (HighPoint Resources) | Rowe, Aryn (HighPoint Resources) | Hodgetts, Stephen (Dolan Integration Group) | Dolan, Michael (Dolan Integration Group)
Scope: The management, treatment and disposal of hydraulic fracturing flowback fluids and produced water presents a major challenge to operators. Though the volumes of water are tracked closely during operations, the sources of that water are not well understood. The objective of this study is to apply a cost effective and proven technique, stable isotope analysis, along with an extensive sampling program (n>1,500 samples) to describe the contributions of variable water sources through completions, flowback and the production lifecycle of multiple horizontal, hydraulically fractured wells in the Denver Basin, Colorado.
Methods: The water stable isotopes of hydrogen (1H and 2H) and oxygen (16O and 18O) are conservative tracers and particularly advantageous because they occur naturally in these systems and rely on well-established scientific and analytical techniques. Sample collection is simple and does not require specialized equipment or operational downtime. 80 horizontal, hydraulically fractured wells completed in the Cretaceous Niobrara or Codell Formations were selected for this study. More than 1,500 samples were collected and analyzed in total, including: baseline samples of the source water used to stimulate the well, time series samples collected at daily or semi-daily intervals during the early weeks of flowback, and samples collected several months after the wells were brought on production. Samples of produced water were also collected from legacy wells in the field as well as offset wells being monitored for frac hits during completions.
Results: Samples of the near surface and shallow aquifer source water collected prior to hydraulic fracturing fell on or near the global meteoric water line (GMWL) as defined by Craig (1961). This isotopic signature is expected for modern water in aquifers charged by precipitation. In contrast, samples collected during flowback and production were significantly enriched in 2H and 18O. Furthermore, the magnitude of the isotopic difference between the source and flowback water increased with time until equilibrating after several months. This equilibrated composition is consistent for Niobrara and Codell wells in the field, as well as legacy wells sampled and consequently is hypothesized to be indicative of native formation water. The study did find exceptions, particularly with wells known to be connected to major fault or fracture networks. These samples deviated from typical formation water signatures, potentially indicating the migration of deeper sourced fluids or the vertical mixing of shallower fluids with Cretaceous waters.
Significance: The scale of this study is unique in the literature and provides novel and comprehensive insight into the dynamics of flowback and the sources of produced water in the Denver Basin. This study demonstrates that these data can clearly differentiate water injected during stimulation from native formation waters, as well as track the magnitude and duration of well cleanup. It can also identify wells that may be producing water with a unique composition due to fluid migration through faults or fracture networks or due to nearby well communication.
Sanguinito, Sean (National Energy Technology Laboratory) | Cvetic, Patricia (National Energy Technology Laboratory) | Goodman, Angela (National Energy Technology Laboratory) | Kutchko, Barbara (National Energy Technology Laboratory) | Natesakhawat, Sittichai (National Energy Technology Laboratory)
It is becoming increasingly important to expand the fundamental understanding of geochemical interactions between CO2, fluids, and shale. These interactions will significantly impact the processes of 1) storing CO2 in hydraulically fractured shale formations, 2) using CO2 as a fracturing agent, and 3) enhancing hydrocarbon recovery in shales via CO2 flooding. In this work, we use in-situ Fourier Transform infrared spectroscopy (FT-IR), feature relocation scanning electron microscopy (SEM), and surface area and pore size analysis using volumetric gas sorption and density function theory (DFT) methods to characterize and quantify the reactions that occur between CO2, fluids, and shale. Several shale samples from across the U.S. were analyzed including the Marcellus, Utica, and Eagle Ford Shales. CO2 will be injected into shale formations where it will interact with shale surfaces (i.e. clays, organic matter), in-situ fluids (i.e. natural brines), and previously injected fracturing fluid. Currently, it is assumed that dry supercritical CO2 does not interact with or have any impact on reservoir rocks or seals. Our suite of measurements show CO2 interaction with clay and kerogen components of the shale, reactivity and etching of carbonate, and changes in pore sizes at the meso- and micro-scale. Very few studies are taking into account the reactivity of CO2 and fluids in the reservoir. The reactions that occur between CO2, fluids, and the shale may alter petrophysical properties such as porosity and permeability which may alter flow pathways potentially impacting the storage permeance of CO2 and the effectiveness of CO2 to behave as a fracturing agent or to mobilize hydrocarbons.
With increasing awareness and concern of CO2 emissions and climate change, there has been a shift in research efforts to evaluate the potential of shales to be used as CO2 storage reservoirs and effective natural seals for CO2 or hydrocarbons (Orr, F.M., 2009a.; Orr, F.M., 2009b; Romanov et al., 2015; Levine et al., 2016, Bacon et al., 2015). Current research is underway to determine the fundamental understanding of geochemical interactions between CO2, fluids, and shale. Fluids, such as formation fluids and fracturing fluids, can react with the CO2 and shale interface to alter formation properties (Jun, Y et al., 2013; Dieterich et al., 2016). This geochemical alteration of shale has been reported to directly affect porosity, permeability, flow paths, and integrity of the wellbore, seal, and formation (DePaolo and Cole, 2013). Additionally, the storage temperature and pressure conditions and the composition and chemistry of brine solution and hydraulic fracturing fluid have an impact on the geochemical alteration of the shale (specifically dissolution).
Hydrocarbon miscible flooding for oil recovery in low permeability reservoirs has proved to be promising in the past few years. Mobility control in this process is crucial for commercial vitality where severe reservoir heterogeneity can cause very poor sweep efficiency. Aqueous foam has been proved effective for conformance control in heterogeneous reservoirs. However, water injection required to sustain foam performance can be challenging in water sensitive tight oil formations. This study introduced a new concept of non-aqueous foam-assisted hydrocarbon miscible flooding in heterogeneous tight oil formations. This process involves the injection of Natural Gas Liquids Mixture (NGLM) as a raw mixture of natural gas liquids, which has been becoming more available with the increase in wet gas, shale plays over the last decade. The injection of non-condensable gas (i.e., nitrogen or methane) and NGLM with a foam stabilizing additive may provide a recovery scheme with both strong displacement and sweep efficiency. The foaming ability of a hydrocarbon liquid and how such non-aqueous foam influences miscible displacement are the main focuses of this study.
n-Pentane was used as a simple NGLM model for the low-pressure foam stability tests. Transient foam propagation and its rheological behavior were directly visualized and characterized based on the apparent gas (nitrogen) viscosity associated with foam texture (i.e., bubble density) and fluid saturations. The impact of foam propagation on miscible displacement efficiency was evaluated by comparing oil recovery factors for different degrees of in-situ foaming.
For the injection of a liquid pentane slug followed by a gas drive, the displacement of oil became unstable as nitrogen fingered through the solvent-oil mixing zone, resulting in a relatively high residual (solvent and oil) saturation. However, gas fingering was significantly hindered as the solvent slug was injected with a dissolved foaming agent. Direct visualization of dynamic fluid distribution during the flooding clearly indicates that the improved recovery could be attributed to the generation and propagation of hydrocarbon foam in the swept zone, which provided essential flow resistance in this zone for better displacement stability. To investigate the effect of pre-injected gas or naturally occurring gas in the reservoir on flow behavior and sweep efficiency, a slug of gas was injected and chased by liquid pentane. Delayed pentane breakthrough and better sweep efficiency were observed in the presence of foam due to the reduced liquid phase mobility in preferential flow paths and the diversion of solvent into small pores by the trapped gas at the displacing front. Furthermore, it was possible to obtain stable foam flow with an apparent viscosity of 20 times greater than the solvent viscosity.
This work provides a pioneer study in applying non-aqueous foam for conformance control in hydrocarbon miscible flooding in tight oil formations. The pore-scale visualization provides a better understanding of the mechanisms of non-aqueous foam generation and destruction in porous media, which have been rarely found in the literature. Furthermore, the experimental results demonstrate that the sweep efficiency and the recovery factor of miscible injection can be significantly enhanced by non-aqueous foam.
The objective of this study was to perform flow simulation based-reservoir modeling on a two-well pad with a long production history and identical completion parameters in the Midland Basin. A reservoir model was built using properties generated from an established geomodel. Sensitivity analysis was performed during early history match to identify ‘heavy hitters’. Subsequent history matching was conducted with less than 10% of global error, and ranges of uncertain parameters have substantially narrowed as a result. The top 50 history-matched models are selected to predict Estimate Ultimate Recovery (EUR) followed by probabilistic analysis that shows P50 of oil EUR is within acceptable range of deterministic EUR estimates. Lateral spacing sensitivity was investigated with the best history-matched model to find the maximum volume and economic benefit by varying lateral spacing of a two-well pad. The results show that, given the current completion design, well spacing tighter than the current development practice in the area is less effective in terms of volume recovery yet economic values suggest that the optimum spacing for the area is around 150% of current development assumption for one section. The presented workflow provides a systematic approach to find the optimum lateral spacing in terms of volume and economic matrices per one section. Change in commodity price will shift optimum well spacing recommendation by suggested workflow. Similar methodology can be readily performed to evaluate spacing optimization in other acreage.
Mackey, Justin (National Energy Technology Laboratory) | Gardiner, James (National Energy Technology Laboratory) | Kutchko, Barbara (National Energy Technology Laboratory) | Brandi, Meghan (National Energy Technology Laboratory) | Fazio, James (National Energy Technology Laboratory) | Hakala, Alexandra (National Energy Technology Laboratory)
This study investigates the impacts injection fluids used in hydro-fracture operations have on mineral scale precipitation within steel production casing of unconventional shale wells. Low carbon steel coupons were reacted with synthetic hydraulic fracturing fluid in an autoclave system set at 50° C and 2000 PSI for 2, 7, 14 and 28 days. Physical and mineralogical changes to the surface of the steel coupons were observed using a FEI Quanta 600 field emission gun (FEG) scanning electron microscope (SEM) and reacted effluent was measured for total Ba, Sr, Fe, Ca, Mg and K as well as dissolved Cl− and SO42−. Results indicate that barite scale precipitates out of fluids commonly used in hydraulic fracturing operations in the absence of reservoir mineralogy. Sulfate radical formation due to degradation of ammonium persulfate breakers within the fluid likely facilitates barite formation. Iron-oxyhydroxide formation from steel corrosion may provide sites for heterogenous nucleation and thus facilitate additional scale growth. Avoidance of persulfate breakers as well as an informed corrosion management program can help mitigate mineral scale hazards in unconventional oil and gas operations.
Hydraulic fracturing is a commonly applied reservoir stimulation technology used to exploit unconventional hydrocarbon deposits. The process of hydraulic fracturing, referred to as hydrofracking or fracking within the oil and gas industry, involves injection of hydraulic fracturing fluids (HFF) (Table 1), a water-based solution with performance additives that is designed to mechanically fracture a petroleum source rock. More recently, fracking has been instrumental to the exploitation of domestic unconventional resources, which serves to expand the domestic energy portfolio and position the United States to become a net energy exporter by 2020 (EIA, 2019a,b). Counter to the benefits, hydrofracking methods of onshore unconventional reservoirs are capital intensive, comprising 60%-70% of the total cost of the well (EIA, 2016). Water procurement, treatment and injection is a significant contributor to the cost of hydraulic fracturing. Hydraulic fracture fluids (HFF) used to fracture geologic formations are a highly variable mixture of fresh water, recycled produced water and chemical additives. The scientific community and industry are still trying to understand what chemical reactions are happening downhole.
With hundreds of rigs running and thousands of wells producing in unconventional plays, more and more data becomes available every day and it is ever more tempting to apply machine learning techniques for unconventional development, be it to identify geology sweet spot, understand performance drivers and optimize development strategies such as well spacing, completion and production designs etc. However, most of the previous applications of machine learning are limited to either certain types of data or small areas of interest. Consequently, the results often lack the predictability or generalizability necessary to impact important development decisions. We developed a flexible, scalable and integrated machine learning framework to leverage all sources of data for the goal of optimizing unconventional resources development.
The framework is built on a big data warehouse and on-demand capability to efficiently visualize and analyze large volumes of heterogeneous data. The most important pillar of the framework is the ability to transform all different types of data with fit-for-purpose methodologies to be closely related to the evaluation and prediction of well performance. This is enabled mechanistically by an interface to scripting languages such as R or Python for interactive data processing, validation and visualization. We also developed several innovative methodologies to overcome some common challenges in characterizing well performance and analyzing well spatial and temporal relationships in terms of well spacing, stacking and infill timing. Ultimately, all the data is regularized to be ready for machine learning. The framework enables a rich set of state-of-the-art machine learning techniques. More importantly, the integration of machine learning with geology, reservoir and development data in a visual environment enables very intuitive and interactive testing, validation and interpretation, which provides valuable insight and confidence for development decision making.
The framework has been extensively employed in Permian Basin for important technical studies such as evaluation of new formation, optimization of well completion and spacing, and even PUD reserve booking compatible with SPEE recommended reliable technology. Field case studies clearly demonstrate the applicability and efficiency of the framework as well as the predictability and insights the machine learning techniques offer.