Near-critical Oil Research Articles

Statistical, Data-Driven Approach to Forecasting Production from Liquid-Rich Shale Reservoirs

The oil and gas industry needs fast and simple techniques of forecasting oil and gas production. Forecasting production from unconventional, low permeability reservoirs is particularly challenging. As a contribution to the continuing efforts of finding solutions to this problem, this paper studies the use of a statistical, data-driven method of forecasting production from liquid-rich shale (LRS) reservoirs called the Principal Components Methodology (PCM). In this study, production of five different highly volatile and near-critical oil wells was simulated for 30 years with the aid of a commercial compositional simulator. Principal Components Methodology (PCM) was applied to production data from the representative wells by using Singular Value Decomposition (SVD) to calculate the principal components (PCs). These principal components were then used to forecast oil and solution gas production from the near-critical oil wells with production histories ranging from 0.5 to 2 years, and the results were compared to simulated data and the Modified Arps’ decline model forecasts. Application of the PCM to field data is also included in this work. Various factors ranging from ultra-low permeability to multi-phase flow effects have plagued the mission of forecasting production from liquid rich shale reservoirs. Traditional decline curve analysis (DCA) methods have not been completely adequate for estimating production from shale reservoirs. The PCM method enables us to obtain the production decline structure that best captures the variance in the data from the representative wells considered. This technique eliminates the need for parameters like the hyperbolic decline exponents (b values) and the task of switching from one DCA model to another. Also, production forecasting can be done without necessarily using diagnostic plots. With PCM, production could be forecasted from liquid-rich shale reservoirs with reasonable certainty. This study presents an innovative and simple method of forecasting production from liquid-rich shale (LRS) reservoirs. It provides fresh insights into how estimating production can be done in a different way.

Depletion oil recovery for systems with widely varying initial composition

The principal depletion drive mechanism is the expansion of oil and gas initially in the reservoir—neglecting water influx. The main factors in depletion drive reservoir performance are total cumulative compressibility, determined mostly by initial composition (gas–oil ratio), saturation pressure, PVT properties, and relative permeability. In this paper, we systematically study the effect of initial composition on oil recovery, all other parameters held constant. We also evaluate other aspects of reservoir performance, but the main emphasis is surface oil recovery including condensate. To analyze the effect of initial composition, a series of fluid systems was selected by a recombination of separator samples at varying gas–oil ratios. The systems ranged from low-GOR oils to high-GOR gas condensates, with a continuous transition from gas to oil through a critical mixture. Black oil and compositional material balance calculations, 2D fine-grid, and 3D coarse-grid models have been used to investigate the effect of initial fluid composition on reservoir depletion performance. Systematic variation of relative permeabilities was also used to map the range of fluid systems, which were most sensitive to relative permeability. For reservoir oils, the depletion recovery of surface oil initially increases with increasing initial gas–oil ratio. Oil recovery reaches a maximum for moderate-GOR oil reservoirs, followed by decreasing oil recoveries with increasing initial solution GOR. A minimum oil recovery is reached at a near-critical oil. For gas reservoirs, depletion drive condensate recovery increases monotonically from a near-critical gas towards near 100% condensate recovery for very-high GOR systems. STO recovery from oil reservoirs depends increasingly on gas–oil relative permeabilities as initial solution GOR increases, up to a point. At higher initial solution GORs, oil recovery becomes less dependent on relative permeability and, as the fluid system transitions to a gas at the critical point, relative permeability dependence rapidly diminishes. Condensate recovery from gas condensate systems is more or less independent of gas–oil relative permeabilities, with only slight dependence for near-critical gases.

Miscibility Variation in Compositionally Grading Reservoirs

Summary Minimum miscibility conditions of pressure and enrichment (MMP/MME) have been computed with an equation of state (EOS) for several reservoir-fluid systems exhibiting compositional gradients with depth owing to gravity/chemical equilibrium. MMP/MME conditions are calculated with a multicell algorithm developed by Aaron Zick, where the condensing/vaporizing (C/V) mechanism of developed miscibility is used as the true measure of minimum miscibility conditions when it exists. The Zick algorithm is verified by detailed one-dimensional (1D) slimtube simulations with elimination of numerical dispersion. The miscibility conditions based on the traditional vaporizing-gas-drive (VGD) mechanism are also given for the sake of comparison, where it is typically found that this mechanism overpredicts conditions of miscibility. Significant variations in MMP and MME with depth exist for reservoirs with typical compositional gradients, particularly for near-critical oil reservoirs and gas-condensate reservoirs where the C/V mechanism exists. An important practical implication of these results is that miscible displacement in gas-condensate reservoirs can be achieved far below the initial dewpoint pressure. The requirement is that the injection gas (slug) be enriched somewhat beyond a typical separator gas composition and that the C/V miscibility mechanism exist. This behavior results in many more gas-condensate reservoirs being viable candidates for miscible gas cycling than previously assumed, and at cycling conditions with lower cost requirements (i.e., lower pressures) and greater operational flexibility (e.g., cycling only during summer months).

Engineering Applications of Phase Behavior of Crude Oil and Condensate Systems (includes associated papers 16046, 16177, 16390, 16440, 19214 and 19893 )

Distinguished Author Series articles are general, descriptiverepresentations that summarize the state of the art in an area of technology bydescribing recent developments for readers who are not specialists in thetopics discussed. Written by individuals recognized as experts in the area, these articles provide key references to more definitive work and presentspecific details only to illustrate the technology. Purpose: to informthe general readership of recent advances in various areas of petroleumengineering. Summary Fluid samples must be taken early in the life of a reservoir toobtain samples truly representative of the reservoir fluid. They should betaken only after a carefully planned well conditioning and testing program. When the PVT data obtained from these samples are used, care should be taken toadjust FVF's and gas/oil ratios (GOR's) for surface separator conditions. Introduction The proper development, engineering, and production of an oil or gasreservoir requires a considerable amount of planning. At the same time thatplans are formulated to develop the field, plans should also be made for adata-gathering program to facilitate reservoir engineering months and evenyears into the future. This data-gathering plan should include a sufficientnumber and variety of electric logs and cores on key wells to describe thereservoir adequately. Electric logs and core analyses evaluate the reservoirrock. If reservoir engineering calculations are to be made to optimizeproduction from a reservoir, including EOR, then the properties of thereservoir fluids must also be known. The properties of the reservoir water fallwithin narrow ranges and are seldom studied at reservoir pressures andtemperatures. The properties of the reservoir water determined are normallyconfined to chemical analysis and possibly compatibility tests in cases ofinjection projects. This paper is concerned primarily with the study of thehydrocarbon fluids contained in a reservoir. Coring and logging programs shouldcontinue throughout the development of a reservoir. The data obtained from thelast well drilled are as valuable as the data obtained from the first well. This is usually not the case for reservoir fluids. Samples representative ofthe original reservoir can be obtained only when the reservoir pressure isequal to or higher than the original bubblepoint or dewpoint. Methods areavailable for extrapolating fluid data obtained after some pressure decline. Remember, however, that extrapolation is an educated man's word for guessing. Plans for obtaining reservoir fluid samples and analyses should be made earlyin the life of a reservoir. Reservoir fluid samples should be taken beforesignificant reservoir pressure decline has been experienced. Oil Reservoirs Oil reservoirs can be divided into two categories: ordinary oil reservoirsand near-critical reservoirs. The ordinary oil reservoirs are sometimes calledblack-oil reservoirs. This misnomer does not reflect the color of the reservoirfluids. It is meant only to distinguish them from near-critical oil reservoirfluids. The near-critical fluids will be discussed in subsequent paragraphs. Ordinary oils are characterized by GOR's up to approximately 2,000 ft3/bbl [360m3/m3], oil gravities up to 45 degrees API [0.8 g/cm3], and FVF's of less than2 bbl/bbl [2 m3/m3]. Remember that there is no sharp dividing line between anordinary and a near-critical oil. Such factors as composition and reservoirtemperature greatly influence the behavior of the reservoir fluid. It is oftenimpossible to determine whether a fluid should be studied as a near-criticaloil or as an ordinary oil until it is actually in the laboratory beingobserved. JPT P. 715^