|Theme||Visible||Selectable||Appearance||Zoom Range (now: 0)|
Miscible injection is a proven, economically viable process that significantly increases oil recovery from many different types of reservoirs. Most miscible flooding projects use CO2 or nitrogen as solvents to increase oil recovery, but other injectants are sometimes used. This page provides an overview of the fundamental concepts of miscible displacement. Also provided are links to additional pages about designing a miscible flood, predicting the benefits of miscible injection, and a summary of field applications. Fieldwide projects have been implemented in fields around the world, with most of these projects being onshore North American fields.
Many analytical methods rely on complete datasets. However, data obtained from the field are often incomplete or inaccurate. An example of this would be well production data, where oil, water and gas rates are provided, but sometimes water or gas rates are missing. With current technology, generative machine learning methods, such as those used for Deep Fake, can generate images and data that are all but indistinguishable from reality. Using an adapted generative method known as the Generative Adversarial Imputation Network (GAIN), this paper evaluates these methods and capabilities for filling in missing data. Validation of these methods was done by creating missing data within complete data sets, comparing the generated values to that of the original.
This work found that with initially large datasets and upwards of 45% of missing values, data can be "filled in" with surprising accuracy. Quantification of the GAIN process and association of missing data and variable importance was done with the use of probability distributions. The relationship between the amount of missing data and the accuracy and probability associated with the predictions has been further quantified and presented within the context of various types of datasets.
This paper discusses how generative methods of machine learning were utilized to fill in the missing portion of existing data with great success. Using a GAIN model, the missing fields can be generated for use in statistical analysis, decision making, and the optimization of current and future projects.
Many studies have been done using machine learning in order to find better and better ways to predict the unknowns. This is true for all fields, and when the data acquired does not always meet the criteria of the researcher, decisions on how to go forward tends to lead to some data just outright getting discarded.
Missing data is a profound issue affecting a number of fields, from agricultural to medical and, of course, particularly data in the oil industry. These missing variables can be problematic when a researcher desires to use all the data they can when doing any sort of data analytics. Suddenly, the researcher is required to make decisions on how to, or whether to, make up for the missing information. That person must make a choice, to take an average of the other available data, come up with a control value to make up for any missing data in a specific variable, or determine if it is the best practice to simply drop that sample of data. For example, if there is a row of data (sample) with 30 data values, and three values are missing, a choice must be made on whether to disregard the entire row of data, or to find suitable values that can be used to replace the missing values.
Several dual- and triple-porosity models have been proposed for quantifying the porosity exponent (m) in multiporosity reservoirs. Total porosity (ø) is usually portioned into the matrix (øb) and vuggy porosity, which includes separate vugs (SVGs) and connected vugs (CVGs). As a result, in their majority, the existing petrophysical models were developed and applied mostly without any distinction between the various types of CVGs despite their specific pore geometries, which critically determine the properties of the rock/fluid systems. For instance, unlike otherwise CVGs, natural fractures (NFs) and microcracks that have low pore-aspect-ratio values are highly compressible; this can cause their closure and lead to increasing m values.
In this paper, we proposed a quadruple-porosity model that accounts for NFs (ø2 or øf) and CVGs (øc), in addition to øb and SVGs (ønc) separately, as distinct input variables to ensure accurate determination of m in composite reservoirs. The approach was based on the volume-model method and rules of electric-resistance networks in porous media. Computed water-saturation values used to validate the model show significant improvement and close agreement with the laboratory measurements, demonstrating the applicability of the proposed model for accurate prediction of m in naturally fractured vuggy reservoirs.
New correlations that consider the pore-type diversity were generated using a plot of ø vs. m, obtained with the proposed quadruple-porosity model. The procedure involved sorting the ø/m scattering points using pore-type mixing and relative abundance of specific porosity. It allowed defining consistent ø/m relationships, with determination coefficients of 0.7 to 0.9. This suggests that m varies with the pore-structure types; this was further demonstrated with a rock-frame flexibility factor (γ) used as a proxy to cluster the scattering points. The established correlations can alternatively be applied to reasonably predict m using detailed prior knowledge of pore-type description.
This article presents brief summaries of detailed petrophysical evaluations of several fields that have been described in the SPE and Soc. of Professional Well Log Analysts (SPWLA) technical literature. These case studies cover some of the complications that occur when making net-pay, porosity, and water saturation (Sw) calculations. Prudhoe Bay is the largest oil and gas field in North America with more than 20 billion bbl of original oil in place (OOIP) and an overlying 30 Tscf gas cap. In the early 1980s, the unit operating agreement required that a final equity determination be undertaken. In the course of this determination, an extensive field coring program was conducted, which resulted in more than 25 oil-based mud (OBM) cores being cut in all areas of the field and some conventional water-based mud (WBM) and bland-mud cores in other wells.
In the early days of the oil industry, saline water or brine frequently was produced from a well along with oil, and as the oil-production rate declined, the water-production rate often would increase. This water typically was disposed of by dumping it into nearby streams or rivers. In the 1920s, the practice began of reinjecting the produced water into porous and permeable subsurface formations, including the reservoir interval from which the oil and water originally had come. By the 1930s, reinjection of produced water had become a common oilfield practice. Reinjection of water was first done systematically in the Bradford oil field of Pennsylvania, U.S.A. There, the initial "circle-flood" approach was replaced by a "line flood," in which two rows of producing wells were staggered on both sides of an equally spaced row of water-injection wells. In the 1920s, besides the line flood, a "five-spot" well layout was used (so named because its pattern is like that of the five spots on ...
Before computer modeling was common, the 3D aspects of a waterflood evaluation were simplified so that the technical problem could be treated as either a 2D-areal problem or a 2D-vertical problem. To simplify 3D to 2D areal, either the reservoir must be assumed to be vertically a thin and homogeneous rock interval (hence having no gravity considerations) or one of the published techniques to handle the vertical heterogeneity and expected gravity effects within the context of a 2D-areal calculation must be used. The primary areal considerations for a waterflood involve the choices of the pattern style (see Figure 1) and the well spacing. Maximizing the ultimate oil recovery and economic return from waterflooding requires making many pattern- and spacing-related decisions when secondary recovery is evaluated. This has been particularly true for onshore oil fields in the US in which a significant number of wells were drilled for primary production.
Situated in central Mexico, Mexico City sits at a minimum elevation of 7,217 ft in the Valley of Mexico, a fitting location for one of North America's most important financial centers. It is truly a global city, in every sense of the term, with its rich background and the continuing diversity of its population. Human history in this area stretches back to nearly 8000 BCE, when agriculture (focusing on crops like squash) was first being implemented in the Americas. The Olmec were the first major civilization in Mexico, and gave rise to some well-known groups such as the Maya, Teotihuacán, Toltec, and Zapotec. Contact with early conquerors and settlers entirely changed the Aztec, the final of these civilizations, and eventually led to the creation of a city ripe for international trade.
Ren, Bo (The University of Texas at Austin) | Male, Frank (The University of Texas at Austin) | Wang, Yanyong (The University of Texas at Austin) | Baqués, Vinyet (The University of Texas at Austin) | Duncan, Ian (The University of Texas at Austin) | Lake, Larry (The University of Texas at Austin)
The objectives of this work are to understand the characteristics of oil saturation in residual oil zones (ROZs) and to optimize water alternating gas (WAG) injection strategies. ROZs occur in the Permian Basin and elsewhere, and operators are using CO2 injection for enhanced oil recovery (EOR) in these zones. ROZs are thought to be formed by the flushing effect of regional aquifer flow acting over geological time. Both the magnitude of oil saturation and the spatial distribution of oil differ from water-flooded main pay zones (MPZs).
We conducted flow simulations of CO2 injection into both synthetic and realistic geologic reservoirs to find the optimal injection strategies for several scenarios. These simulations of CO2 injection follow either man-made waterflooding or long-term natural waterflooding. We examined the effects of CO2 injection rates, well patterns, reservoir heterogeneity, and permeability anisotropy on optimal WAG ratios. Optimal is defined as being at minimal net CO2 utilization ratios or maximal oil production rates).
Simulations of CO2 EOR show that the optimal WAG ratio for the ROZs is less than 1 (ratio of injected water and CO2 in reservoir volumes), and it depends, but in qualitatively different ways, upon the well pattern and reservoir heterogeneity. The optimal WAG ratio tends to increase with changing from inverted 9-spot (80-acres) to inverted 5-spot (40-acre) or increasing reservoir heterogeneity. The ratios for ROZs are consistently less than those observed in the same geologic models experiencing CO2 injection after traditional (man-made) waterflooding. This is because the water saturation caused by slow regional aquifer flow (~1ft/yr) differs from that created by traditional waterflooding. In ROZs, water prevails almost everywhere and thus it is less needed to ease CO2 channeling as compared to MPZs.
This work demonstrates that optimal WAG ratios for oil production in ROZs are different from those in traditional MPZs because of oil saturation differences. Thus, commingled CO2 injection into both zones or directly copying WAG injection designs from MPZs to ROZs might not optimize production.
The combination of extended-length horizontal drilling and high volume hydraulic fracturing has led to previously unimaginable production increases, yet the recovery potential of unconventional oil and gas resources remains largely unrealized. Recovery factors for unconventional oil and gas wells are typically reported at < 20% in gas shale reservoirs and < 10% in the oil plays.
Neutrally buoyant ultra-lightweight proppants have been demonstrated to effectively provide production from fracture area that is otherwise unpropped and thus, non-contributive with conventional sand/slickwater hydraulic fracturing processes. Production simulations illustrate that treatment designs incorporating neutrally buoyant ULW proppant treatment designs tailored for contemporary unconventional well stimulations deliver cumulative production increases of 30% to over 50% compared to the typical large volume sand/slickwater treatments. Unfortunately, production simulation results may not sufficiently lessen risk uncertainties for operators planning high-cost multi-stage horizontal stimulations. Therefore, several field trial projects using the neutrally buoyant ULW proppant in extended-length horizontal unconventional wells are currently in progress to validate the production simulations.
Since the initial 4-stage fracturing stimulation incorporating neutrally buoyant ultra-lightweight proppant in 2007, deployment has occurred in fracture stimulating hundreds of oil and gas wells spanning multiple basins and reservoirs. Most of the wells are vertical or relatively short lateral wells common to asset development practices predating the unconventional shale completions mania, but many were targeted at the same unconventional reservoirs as the current multi-stage horizontal completions. Several published case histories have documented the production enhancement benefits afforded by the legacy ULW proppant wells, but questions remained as to how those lessons might be correlated to provide engineers confidence in the current production simulations.
Well completion and production information was mined from the various accessible databases for the neutrally buoyant ULW proppant wells. The scope of the legacy data compiled for analysis was limited to the reservoirs common to the current field trials and production simulations, ie. unconventional oil and gas shale reservoirs. Production performance contributions of neutrally buoyant ULW proppant in past applications were compared with the production uplift observed in applications and/or simulated application of neutrally buoyant ultra-lightweight proppant fracturing treatments in current multi-stage horizontal reservoirs.
The lessons learned from this investigation provide the practicing engineer the means to confidently assess production simulation data for multi-stage horizontal unconventional completions incorporating neutrally buoyant ulw proppant in the treatment designs.
Standard approaches to optimization under uncertainty in reservoir simulation require use of multiple realizations, with variable parameters representing operational constraints and actions as well as uncertain scenarios. We will show how appropriate use of local optimization within the simulation model, using customized logic for field management strategies, can bring improved workflow flexibility and efficiency, by reducing the effort needed for uncertainty iterations.
To achieve meaningful forecasts for an ensemble of uncertain scenarios, it is important to distinguish between different types of decision. Investment decisions, such as facilities sizing, depend on global unknowns and must be optimized for the complete ensemble. Operational actions, such as closing a valve, can be optimized instantaneously for individual scenarios, using measurable information, although subject to constraints determined at a global level. In this study, we implement local optimization procedures within simulation cases, combining customized objective criteria to rank reactive or proactive actions, with the ability to query reservoir flow entities at appropriate frequencies.
The methods presented in the paper can be used for reactive response modeling for smart downhole control; optimization of ESP/PCP pump performance; and implementation of production plans subject to defined downstream limits. For selected cases, we compare the advantages and disadvantages of the local optimization approach with standardized "big-loop" uncertainty workflows. The methodology can significantly reduce optimization costs, particularly for high-frequency actions, achieving similar objective function values in a fraction of the time needed for post-processing optimizers. Use of tailored scripting provides the capability to modernize the logic framework for field management decisions, with realistic representation of smart field equipment and flow entities at any level of complexity.
Use of efficient workflows as described in this paper can reduce the cost of multiple realization studies significantly, or enable engineers to consider a wider range of possible scenarios, for deeper understanding and better risk mitigation.