Achieving high hydrocarbon recovery is challenging in unconventional tight and shale reservoirs. Although EOR/EGR processes could potentially improve the recovery factor beyond the primary depletion, large-scale field application of these processes are not yet established in these reservoirs. This session will focus on the latest research trends, modelling and experimental work to better understand issues involved in improved economic recovery from such reservoirs.
This page discusses single phase permeability models that are specifically based on the pore dimensions of the reservoir. Pore dimensions are a critical factor in determining crucial characteristics of the reservoir; including porosity, permeability, and capillary pressure. The dimension of interconnected pores plays a major role in determining permeability. Most methods of estimating permeability are indirect methods. A viable direct method requires both adequate theoretical underpinnings relating pore throat dimension to permeability and experimental determination of the critical pore dimension parameters.
Various methods that link a representative pore-throat size to permeability k and porosity ϕ have been proposed in the literature for rock typing (i.e., identifying different classes of rocks and petrofacies). Among them, the Winland equation has been used extensively, although when it was first proposed, it was based on experiments. Because of empiricism, the interpretation of the parameters of the Winland model and their variations from one rock sample or even one rock type to another is not clear. Therefore, the main objectives of this study are (1) to propose a new theoretical approach for identifying rock types that is based on the permeability k and the formation-resistivity factor F and (2) to provide theoretical insights into, and shed light upon, the parameters of the Winland equation, as well as those of other empirical models. We present a simple, but promising, framework and show that accurate identification of distinct petrofacies requires knowledge of the formation factor, which is measured routinely through petrophysical evaluation of porous rocks. We demonstrate that, although some rock samples might belong to the same type on the k-vs.1/F plot, they might appear scattered on the k-vs.-ϕ plot and, thus, could seemingly correspond to other types. This is because both k and F are complex functions of the porosity, whereas the porosity itself is simply a measure of the pore volume (PV), and does not provide information on the dynamically connected pores that contribute to both k and F. We also show that each rock can be represented by a characteristic pore size Λ, which is a measure of dynamically connected pores. Accurate estimates of Λ indicate that it is highly correlated with the permeability.
This course provides attendees with a comprehensive methodology for well performance analysis with specific focus on unconventional oil and gas. The approach combines the use of several powerful techniques and will illustrate the practical aspects of production data analysis. Depending on interest and time available, examples from Barnett, Bakken, Montney, Horn River, Marcellus, Haynesville, and Eagle Ford plays will be presented. If you’d like to get more mileage from your production and flowing pressure data, this course is for you. This course is for engineers and technologists involved in exploitation, evaluating reserves, optimising production or analysing well tests.
Variable depositional cycles and severe diagenesis are among the main contributing factors to the complex pore networks encountered in formations, such as carbonates. This complexity is often not reliably incorporated in conventional permeability models. Conventional methods for permeability assessment, including electrical-based models (e.g., Katz and Thompson) and nuclear magnetic resonance (NMR)-based models (e.g., Coates and Schlumberger-Doll-Research), either require characterization of the pore network or calibration efforts, such as detection of cutoff values and assessment of constant model parameters. Joint evaluation of dielectric permittivity, resistivity, and NMR measurements enables capturing pore-network connectivity, tortuosity, and pore-throat-size distribution for real-time and reliable permeability evaluation.
In this paper we (a) estimate parameters that quantify rock fabric (e.g., tortuosity, effective pore size, and pore-throat- size distribution) by joint interpretation of electrical resistivity, dielectric permittivity, and NMR measurements, (b) develop a new workflow for permeability assessment that incorporates rock fabric parameters, and (c) validate the reliability of the new workflow in pore- and core-scale domains using electrical resistivity, dielectric-permittivity, NMR, mercury injection capillary pressure (MICP), and permeability measurements. To achieve these objectives, we introduce a workflow to estimate rock fabric properties as inputs for permeability assessment. NMR measurements are used to estimate porosity and effective pore size. Dielectric-permittivity and resistivity measurements are used to estimate tortuosity and constriction factor. Then, we calculate pore-throat-size distribution from the constriction factor and effective pore size. Finally, the aforementioned rock fabric parameters are used to estimate permeability without calibration efforts.
We successfully validated the introduced workflow on core samples from different lithofacies. Estimates of pore-throat radius obtained using the new method are in agreement with those from MICP measurements. We also applied the new workflow in the pore-scale domain using directional resistivity results obtained from numerical simulations as inputs. We demonstrate that directional-permeability estimates obtained from the introduced workflow in the pore-scale domain agree well with the actual permeability of the samples obtained from numerical simulations in the x-, y-, and z-directions. The proposed workflow reduced the relative error in permeability estimates by 50% in the pore-scale domain, compared to the conventional methods based on porosity-permeability correlations. It also resulted in average relative error of less than 20% in permeability estimates in the core-scale domain. Furthermore, the new workflow eliminates the need for calibration efforts in permeability assessment by honoring and quantifying rock fabric and enables assessment of directional permeability, if directional-resistivity measurements are available.
The fracturing of horizontal wells is a recently developed tool to help enable tight and shale formations to produce economically. Production data analysis of the wells in such formations is frequently performed using analytical and semi-analytical methods. However, in the presence of nonlinearities such as multi-phase flow and geomechanical effects, the numerical simulations are necessary for interpretations and history-matching techniques as they are required for model calibration.
Reservoir history-matching techniques are usually based on the frequentist approach and can provide a single solution that can maximize the Likelihood function. Production forecasts using a single calibrated model cannot honor the uncertainty in the model parameters. Therefore, a Bayesian approach is suggested where we can combine our prior knowledge about the model parameters together with the Likelihood to update our knowledge in light of the data. The Bayesian approach is enriched by applying a Markov chain Monte Carlo process to updated the prior knowledge and approximate the posterior distributions.
In this paper, a one-year production data of a real gas condensate well in a Canadian tight formation (lower Montney Formation) is considered. This is a horizontal well with eight fracture stages. A representative 2D model is constructed which is characterized by 17 parameters which include relative permeability curves, capillary pressure, geomechanical effects, fracture half-length, fracture conductivity, and permeability and water saturation in the stimulated region and the matrix. Careful analysis of available data provide acceptable prior ranges for the model parameters using non-informative uniform distributions. Markov chain Monte Carlo algorithm is implemented using a Gibbs sampler and the posterior distributions are found. The results provide an acceptable set of models that can represent the production history data. Using these distributions, a probabilistic forecast is performed and P10, P50 and P90 are estimated.
This paper highlights the limitations of the current history-matching approaches and provides a novel workflow on how to quantify the uncertainty for the shale and tight formations using numerical simulations to provide reliable probabilistic forecasts.
There are several different technologies available for completing and stimulating multi-stage horizontal wells. By far the most common of these is to use a cemented liner with multiple perforation clusters treated simultaneously in a single stage (plug-and-perf). One alternative method gaining popularity also employs the use of a cemented liner but with sliding sleeves allowing for single point entry into the formation (pinpoint). The objective of this paper is to compare the expected and observed reservoir performance resulting from each of these approaches.
To accomplish the objectives, two methods are appliedtheoretical and experimental. The theoretical approach uses a hydraulic fracture simulator to predict fracture geometry for both plug-and-perf and pinpoint completion techniques for a predefined set of treatment parameters. A reservoir simulator is then used to predict production and ultimate recovery for each case. The experimental approach involves choosing a drilling spacing unit (DSU) where these two completion techniques have been applied (in close proximity) while controlling, or normalizing for, as many other reservoir and completion variables as possible. The well performance data is analyzed using rate transient analysis (RTA), and the results compared to the predictions made by the theoretical models.
Theoretical modeling predicts that slightly better reservoir performance ought to be obtained in pinpoint completions, over plug-and-perf. Experimental (empirical) analysis of actual well performance data supports the theoretical predictions directionally, but significantly exceeds the uplift predicted by the theoretical models. Ideally, the experimental data should be collected under controlled conditions. In reality, this is not the case as operations on a typical well pad (as is the case in this study) are continuously subjected to disturbances, unconstrained variables and incomplete and/or inaccurate measurements. Thus, results from RTA are somewhat subjective and error prone. The confidence of these results improve dramatically as the sample data set increases.
To our knowledge, this work represents the first objective comparison of different completion types using rate transient analysis as an evaluation tool. The experimental benchmarking procedure introduced in this work is novel and represents a significant improvement over existing industry standards for understanding how completion technology can impact well performance.
This case study reviews the full development cycle of Devon Energy's Parkman asset in the Powder River Basin, from exploitation to infill drilling. The focus is on successive adjustments to drilling and completion design, with the objective of net-present-value (NPV) optimization. This effort is supported primarily through timely collection and interpretation of data, which has awarded Devon with exceptional returns on investment with reasonably low risk and capital expenditure, even in a low oil price environment. Devon's success in the Parkman is largely due to the practical and adaptive nature of the underlying engineering workflows supporting key decisions as well as management's strong support for the value of information.
The methodology is based on an integrated workflow, combining core, open-hole logs, reservoir surveillance (radioactive tracers and fiber optics) along with production and flowing pressure data. Rate Transient Analysis, fracture modeling and reservoir simulation are central components of the integrated model. At every stage in the development cycle, the model is calibrated to the latest data, at which point sensitivities can be run to evaluate alternative scenarios for optimizations required for the next stage. Through successive drilling and completion improvements, Devon has successfully increased the NPV of their Parkman asset. The steps toward optimization include increasing lateral length from 4,000' to 9,500'; adjusting cluster spacing between 60' and 100' and transitioning their completion method from plug-andperf to pinpoint. Understanding the impact of oil price is critical for achieving these optimizations. The improvements were supported by reservoir simulation and ultimately confirmed with field data, which is included in this work.
This work brings to the forefront lessons that could add significant value to the business of any unconventional operator: A practical methodology for quickly converting collected data into actionable knowledge; allowing continual adjustments to be made to an unconventional completion design that maximize its performance and economic benefit Integration of engineering and geoscience disciplines to better understand the major influences on well performance in an unconventional play Optimizing completion design to maximize the profitability of a field development plan. In the case of the Parkman, pinpoint proves to be a more optimal completion technique
A practical methodology for quickly converting collected data into actionable knowledge; allowing continual adjustments to be made to an unconventional completion design that maximize its performance and economic benefit
Integration of engineering and geoscience disciplines to better understand the major influences on well performance in an unconventional play
Optimizing completion design to maximize the profitability of a field development plan. In the case of the Parkman, pinpoint proves to be a more optimal completion technique