The new-generation oil-base mud (OBM) microresistivity imagers provide photorealistic high-resolution quantified formation imaging. One of the existing interpretation methods is based on composite processing providing an apparent resistivity image largely free of the standoff effect. Another one is the inversion-based workflow, which is an alternative quantitative interpretation, providing a higher quality resistivity image, button standoff, and formation permittivities at two frequencies. In this work, a workflow based on artificial neural networks (NNs) is developed for quantitative interpretation of OBM imager data as an alternative to inversion-based workflow.
The machine learning approach aims to achieve at least the inversion-level quality in formation resistivity, permittivity, and standoff images an order of magnitude faster, making it suitable for implementation on automated interpretation services as well as integration with other machine learning based algorithms. The major challenge is the underdetermined problem since OBM imager provides only four measurements per button, and eight model parameters related to formation, mud properties, and standoff need to be predicted. The corresponding nonlinear regression problem was extensively studied to determine tool sensitivities and the combination of inputs required to predict each unknown parameter most accurately and robustly. This study led to the design of cascaded feed-forward neural networks, where one or more model parameters are predicted at each stage and then passed on to following steps in the workflow as inputs until all unknowns are accurately obtained.
Both inverted field data sets and synthetic data from finite-element electromagnetic modeling were used in multiple training scenarios. In the first strategy, field data from few buttons and existing inversion results were used to train a single NN to reproduce standoff and resistivity images for all other buttons. Although the generated images are comparable to images coming from inversion, the method is dependent on the availability of field data for variable mud properties, which at the moment limits the generalization of the NNs to diverse mud and formation properties.
In the second strategy, we utilized the synthetic responses from a finite element model (FEM) simulator for a wide range of standoffs, formation, and mud properties to develop a cascaded workflow, where each stage predicts one or more model parameters. Early stages of the workflow predict the mud properties from low formation resistivity data sections. NNs then feed the estimated mud angle and permittivities at two frequencies into next stages of the workflow to finally predict standoff, formation resistivity, and formation permittivities. Knowledge of measurement sensitivities was critical to design the efficient parameterization and robust cascaded neural networks not only due mathematically underdetermined nature of the problem but also the wide dynamic range of mud and formation properties variation and the measurements. Results for processed resistivity, standoff, and permittivity images are presented, demonstrating very good agreement and consistency with inversion-generated images. The combination of two strategies, training on both synthetic and field data, can lead to further improvement of robustness allowing customization of interpretation applications for specific formations, muds, or applications.
Recent studies have indicated that Huff-n-Puff (HNP) gas injection has the potential to recover an additional 30-70% oil from multi-fractured horizontal wells in shale reservoirs. Nonetheless, this technique is very sensitive to production constraints and is impacted by uncertainty related to measurement quality (particularly frequency and resolution), and lack of constraining data. In this paper, a Bayesian workflow is provided to optimize the HNP process under uncertainty using a Duvernay shale well as an example.
Compositional simulations are conducted which incorporate a tuned PVT model and a set of measured cyclic injection/compaction pressure-sensitive permeability data. Markov chain Monte Carlo (McMC) is used to estimate the posterior distributions of the model uncertain variables by matching the primary production data. The McMC process is accelerated by employing an accurate proxy model (kriging) which is updated using a highly adaptive sampling algorithm. Gaussian Processes are then used to optimize the HNP control variables by maximizing the lower confidence interval (μ-σ) of cumulative oil production (after 10 years) across a fixed ensemble of uncertain variables sampled from posterior distributions.
The uncertain variable space includes several parameters representing reservoir and fracture properties. The posterior distributions for some parameters, such as primary fracture permeability and effective half-length, are narrower, while wider distributions are obtained for other parameters. The results indicate that the impact of uncertain variables on HNP performance is nonlinear. Some uncertain variables (such as molecular diffusion) that do not show strong sensitivity during the primary production strongly impact gas injection HNP performance. The results of optimization under uncertainty confirm that the lower confidence interval of cumulative oil production can be maximized by an injection time of around 1.5 months, a production time of around 2.5 months, and very short soaking times. In addition, a maximum injection rate and a flowing bottomhole pressure around the bubble point are required to ensure maximum incremental recovery. Analysis of the objective function surface highlights some other sets of production constraints with competitive results. Finally, the optimal set of production constraints, in combination with an ensemble of uncertain variables, results in a median HNP cumulative oil production that is 30% greater than that for primary production.
The application of a Bayesian framework for optimizing the HNP performance in a real shale reservoir is introduced for the first time. This work provides practical guidelines for the efficient application of advanced machine learning techniques for optimization under uncertainty, resulting in better decision making.
Analytically-derived criteria are presented for the orientation of fracture initiation from horizontal wellbores drilled in porous-permeable (poroelastic) media. This involves drilling-induced tensile fractures (DITFs) from non-perforated wellbores and completion-induced hydraulic fractures (CIHFs) from perforated wellbores with cylindrical perforation geometry. The criteria are developed considering the tangential stresses on two points (extremes) around the base of the perforation; one for the initiation of longitudinal fractures and another for the initiation of transverse fractures, with respect to the wellbore. In-situ stress state, wellbore pressure, and the formation's mechanical and poroelastic properties are independent variables that are shown to control the orientation of the initiated hydraulic fractures; the dependent variable.
The DITF orientation can be used to constrain the magnitude of the maximum horizontal stress; the most difficult aspect of the in-situ stress tensor to constrain. Transverse CIHF initiation only occurs over a narrow wellbore pressure-at-breakdown window, while longitudinal initiation occurs at comparatively higher wellbore pressures. However, transverse CIHF initiation occurs more frequently than transverse DITFs, because the presence of perforations aids transverse fracture initiation. The region of the in-situ stress states where transverse initiation is promoted is shown in dimensionless plots for perforated and non-perforated wellbores. Fracture initiation criteria for specific cases presented can be used to predict the orientation of fracture initiation in oilfield operations.
The orientation of CIHFs controls the productivity of hydrocarbon reservoirs. Productivity from low permeability formations is greatly improved having multiple fractures oriented transversely rather than longitudinally, relative to a horizontal wellbore. Fracture initiation often follows a plane different to the final fracture propagation plane. Stress re-orientation in the near-wellbore region may promote fracture initiation of different orientation than the orientation dictated by the far-field stresses. The range of in-situ stress states in which transverse fracture initiation is promoted increases as Biot's poroelastic coefficient,
Mahzari, Pedram (Department of Earth Sciences, University College London) | Oelkers, Eric (Department of Earth Sciences, University College London) | Mitchell, Thomas (Department of Earth Sciences, University College London) | Jones, Adrian (Department of Earth Sciences, University College London)
During the past decade, enhanced oil recovery (EOR) by CO2 in shale oils has received substantial attention. In shale oil reservoirs, CO2 diffusion into the resident oil has been considered as the dominant interaction between the CO2 in fractures and the oil in the matrices. CO2 diffusion will lead to oil swelling and improvement in oil viscosity. However, despite two-way mass transfer during CO2 EOR in conventional oil reservoirs, one-way mass transfer into shale oils saturated with live oils is controlled by an additional transport mechanism, which is the liberation of light oil components in the form of a gaseous new-phase. This
Taking account of Bakken shale oil reservoir data, numerical simulations were performed to identify efficiencies of EOR by CO2 at the laboratory and field scales. Equation of state parameters between CO2 and oil components were adjusted to optimize the calculations and a sensitivity analysis was performed to identify the role of gas formation and consequent EOR efficiencies. At the laboratory scale, in-situ gas formation can increase oil recovery by 20% depending on the amount of gas saturation. Also, the CO2 storage capacity of the shale matrix can be enhanced by 25%, due to CO2 trapping in the gas phase. At the field scale, an additional oil recovery of 9.1% could be attained, which is notably higher than previous studies where this gas evolution mechanism was ignored. Furthermore, the results suggest that a six-weeks huff period would be sufficient to achieve substantial EOR if this new mechanism is incorporated. On the other hand, the produced fluid in the early period was primarily composed of CO2, which would make it available for subsequent cycles. The produced gas of the well under CO2 EOR was used in an adjacent well, which resulted in similar additional oil recovery and hence, impurities in CO2 injection stream would not undermine efficiency of this EOR method. The results of this study, therefore, could potentially be used to substantially improve the evaluations of CO2 EOR in liquid-rich shale reservoirs.
Using planar fracture models to match treatment pressure and improve understanding of the fracture geometry generation is not a new concept. Knowledge gained from this exercise has historically been used to improve engineered fracture completions and production, and maximize net present value (NPV); however, at some point during the progression from vertical to horizontal wellbores, many within the industry have forgotten about the learnings that can still be gained from current fracture models. Engineered completions have been largely replaced by spreadsheet efficiencies relevant to operations rather than production in too many cases. Some images of unconventional well stimulation treatments portray fractures growing in every direction, forming patterns that resemble shattered windshields, and have often excluded the known physics related to rock geomechanics, reservoir properties, and geology. Excuses to dismiss modeling are numerous and are gaining the reasoning of conformists.
Unconventional resource plays might or might not contain large numbers of natural fractures; but, current fracture models can still be used to gain insight into the fracture geometries being generated. While the development of complex fracture models continues to evolve, the industry can still gain insight to fracture geometry and resulting production using current planar fracture modeling. Caveats to this process are that it requires: Valid measured data to establish model constraints. The engineer to understand the basic physics of how fractures are generated and when (and when not) to twist the "knobs" in the model. The engineer to understand which "knobs" should be used based on real diagnostics information. The actual single well production to be an integral part of the process.
Valid measured data to establish model constraints.
The engineer to understand the basic physics of how fractures are generated and when (and when not) to twist the "knobs" in the model.
The engineer to understand which "knobs" should be used based on real diagnostics information.
The actual single well production to be an integral part of the process.
This paper demonstrates the results of honoring data measurements from a multitude of potential sources, including downhole microseismic data, downhole deformation tiltmeters, offset pressure monitoring, DTS, DAS, diagnostic fracture injection test (DFIT) analysis, injection as well as production data with bottomhole pressure measurements, etc., and the resulting observations and conclusions. Several industry examples are discussed to help frame the vast amount of information possible to help engineers do a better job of including more diagnostics into routine operations to provide additional insight and ultimately result in improved models and completion designs.
This paper is not intended to merely demonstrate the results of the work but to spark an interest in bringing more intense engineering back to fracture stimulation modeling for horizontal completions.
Producers in Oklahoma’s newly opened Merge play are sitting atop a resource that rivals some major world gas fields and discoveries, Citizen Energy’s Geology CEO Greg Augsburger told the SPE Gulf Coast Section Business Development Group recently. Linn Energy recently sold its Williston Basin properties for $285 million. This deal brings Linn’s year-to-date total sales agreements to more than $1.5 billion as it financially restructures after bankruptcy. At the recent Leaders in Industry luncheon in Houston, Jonny Jones told the interesting story of how Jones Energy grew from a small private entity to a company whose shares trade on the New York Stock Exchange.
Producers in Oklahoma’s newly opened Merge play are sitting atop a resource that rivals some major world gas fields and discoveries, Citizen Energy’s Geology CEO Greg Augsburger told the SPE Gulf Coast Section Business Development Group recently. Linn Energy recently sold its Williston Basin properties for $285 million. This deal brings Linn’s year-to-date total sales agreements to more than $1.5 billion as it financially restructures after bankruptcy.
A new paper from ADI Analytics examines the circumstances in which natural gas could become an attractive method for fueling oilfield equipment. These balls have the potential to alter how pipeline inspections are done, and a consortium of pipeline operators and industry experts in North Dakota is examining just how well this emerging technology can handle the small-diameter pipelines in the area. The CEO of Phillips 66 expects the DAPL to be operational in 2Q 2017. The Dakota Access Pipeline, which will connect the Bakken and Three Forks production areas, has received the regulatory go-ahead from the US Army Corps of Engineers. This article explores the outlook for the global market and gives insight into technology trends and the regions that hold the biggest opportunities for water treatment.
Linn Energy recently sold its Williston Basin properties for $285 million. This deal brings Linn’s year-to-date total sales agreements to more than $1.5 billion as it financially restructures after bankruptcy. It is known that a well injecting a lot of water near a big fault can lead to earthquakes. The problem is, more often than not those faults are not known until after a tremor. Top US seismic experts say they are keeping a watchful eye on ground shaking in the state as new concerns are raised in neighboring Texas.
Producers in Oklahoma’s newly opened Merge play are sitting atop a resource that rivals some major world gas fields and discoveries, Citizen Energy’s Geology CEO Greg Augsburger told the SPE Gulf Coast Section Business Development Group recently. Linn Energy recently sold its Williston Basin properties for $285 million. This deal brings Linn’s year-to-date total sales agreements to more than $1.5 billion as it financially restructures after bankruptcy. The United States’ liquids-rich shale experience has been dominated by three major plays: the Eagle Ford Shale in south Texas, the Permian Basin in west Texas, and the Bakken Shale that straddles North Dakota and Montana.