|Theme||Visible||Selectable||Appearance||Zoom Range (now: 0)|
Probable reserves are those unproved reserves which analysis of geological and engineering data suggests are more likely than not to be recoverable. In this context, when probabilistic methods are used, there should be at least a 50% probability that the quantities actually recovered will equal or exceed the sum of estimated proved plus probable reserves. In general, probable reserves may include (1) reserves anticipated to be proved by normal step-out drilling where sub-surface control is inadequate to classify these reserves as proved, (2) reserves in formations that appear to be productive, based on well log characteristics, but lack core data or definitive tests and which are not analogous to producing or proved reservoirs in the area, (3) incremental reserves attributable to infill drilling that could have been classified as proved if closer statutory spacing had been approved at the time of the estimate, (4) reserves attributable to improved recovery methods that have been established by repeated commercially successful applications when (a) a project or pilot is planned, but not in operation and (b) rock, fluid, and reservoir characteristics appear favorable for commercial application, (5) reserves in an area of the formation that appears to be separated from the proved area by faulting and the geologic interpretation indicates the subject area is structurally higher than the proved area, (6) reserves attributable to a future workover, treatment, re-treatment, change of equipment, or other mechanical procedures, where such procedure has not been proved successful in wells which exhibit similar behavior in analogous reservoirs, and (7) incremental reserves in proved reservoirs where an alternative interpretation of performance or volumetric data indicates more reserves than can be classified as proved. Often referred to as P2 (SPE).
When a panel of fracturing technology leaders was asked if classic physics-based engineering matters in engineering fracturing, the answer was a qualified "sometimes." The group of three engineers speaking at the start of the SPE Hydraulic Fracturing Technology Conference was not going to dismiss the need for physics-based modeling. Still, applying the physics of flow in a complex, fractured reservoir sounded like a wrong turn. To explain further, Cameron Rempel, vice president for subsurface engineering for Occidental Petroleum, compared analysis at the fracture level to trying to understand rush hour traffic by tracking each person as they pack up in their cubicle and head for their car at the end of the day. That example, which will someday again represent office reality, is both incredibly hard to measure and analyze and does not offer a direct path to answer an analogous question that matters to oil producers: How can we measure the time it takes for all those cars to flow out of downtown and find ways to speed them up?
ABSTRACT The industry is facing significant challenges due to the recent downturn in oil prices, particularly for the development of tight reservoirs. It is more critical than ever to 1) identify the sweet spots with less uncertainty and 2) optimize the completion-design parameters. The overall objective of this study is to quantify and compare the effects of reservoir quality and completion intensity on well productivity. We developed a supervised fuzzy clustering (SFC) algorithm to rank reservoir quality and completion intensity, and analyze their relative impacts on wells' productivity. We collected reservoir properties and completion-design parameters of 1,784 horizontal oil and gas wells completed in the Western Canadian Sedimentary Basin. Then, we used SFC to classify 1) reservoir quality represented by porosity, hydrocarbon saturation, net pay thickness and initial reservoir pressure; and 2) completion-design intensity represented by proppant concentration, number of stages and injected water volume per stage. Finally, we investigated the relative impacts of reservoir quality and completion intensity on wells' productivity in terms of first year cumulative barrel of oil equivalent (BOE). The results show that in low-quality reservoirs, wells' productivity follows reservoir quality. However, in high-quality reservoirs, the role of completion-design becomes significant, and the productivity can be deterred by inefficient completion design. The results suggest that in low-quality reservoirs, the productivity can be enhanced with less intense completion design, while in high-quality reservoirs, a more intense completion significantly enhances the productivity. Keywords Reservoir quality; completion intensity; supervised fuzzy clustering, approximate reasoning,tight reservoirs development
Abstract Market-induced production shut-downs and restarts offer us an opportunity to gather step-rate and shut-in data for pressure transient analysis (PTA) and rate transient analysis (RTA). In this study, we present a unified transient analysis (UTA) to combine PTA and RTA in a single framework. In this new approach continuous production data, step-rate data, shut-in data and re-start data can be visualized and analyzed in a single superposition plot, which can be used to estimate both and infer formation pore pressure in a holistic manner by utilizing all available data. Most importantly, we show that traditional log-log and square root of time plots can lead to false interpretation of the termination of linear-flow or power-law behavior. Field cases are presented to demonstrate the superiority of the newly introduced superposition plot, along with discussion on the calibration of long-term bottom-hole pressure with short-term measurements.
Abstract Assessment of in-situ stresses and hydraulic fracturing stimulation are two critical parameters for successful heat extraction from Enhanced Geothermal Systems (EGS). Fracture injection and injection/flow back tests are two conventional techniques for estimating the minimum horizontal stress in subsurface formations. Because of the heat exchange during the test, ultra-low permeability of the host rock, and natural fractures, the conventional methods yield inaccurate results in geothermal reservoirs. In this paper, we present a new methodology based on the signal processing approach for analyzing DFIT in geothermal reservoirs. The applicability of our technique is demonstrated using several test data from the Utah FORGE project. The main advantage of our methodology is that it does not depend on any assumption regarding fracture geometry and rock properties. Also, unlike most similar studies, we consider the effect of heat exchange between fracturing fluid and the hot rock. In our methodology, the recorded pressure and temperature are treated as signals, and a wavelet transform is applied to separate them to high pass (noise) and low pass (approximation) components. Using the noise energy of the two signals, we then identify different events such as fracture closure. Also, an analytical technique is used to correct the pressure by extracting the effect of fluid compressibility and heat exchange between the rock and injected fluid. We show that the G-Function technique underestimates the minimum horizontal stress in tight formations. After applying the corrections for pressure, the underestimation becomes more apparent. However, our approach gives consistent results before and after the pressure correction. Using the developed technique, we analyzed several injection tests from the Utah FORGE project. Both recorded pressure and temperature have been analyzed. Results show that the energy of the pressure signal noise decreases to a minimum level at the fracture closure. The fracture closure is confirmed by applying the same technique on the recorded temperature. The moment of closure using the proposed methodology is compared to the G-function approach, before and after correction of the pressure for temperature. Unlike physics-based techniques, the proposed method does not have any pre-assumption about the fracture's geometry or type of the well. The method solely relies on the pressure and temperature signals that are recorded during the injection and shut-in periods. Combining several analysis techniques to analyze DFIT (including the analysis of monitored temperature for a geothermal reservoir) is unique and maybe the first of its kind.
Summary Reservoir depletion is known to reduce the porosity and permeability of stress-sensitive reservoir rocks. The effect may substantially hinder the productivity index (PI) of producing wells. This study presents analytical solutions for the time-dependent and steady-state well PIs, respectively, of a bounded, disk-shaped, elastic reservoir with no-flow and constant-pressure conditions at the outer boundary. A combination of Green's functions, the Laplace transform method, and the perturbation technique is used to solve the governing nonlinear partial differential equations of the considered coupled problems of flow and geomechanics. Dimensional analyses based on the Buckingham theorem are conducted to identify the dimensionless parameters groups of each problem and to express the resulting analytical solutions in the dimensionless form. In addition, necessary corrections to an existing error in the reported Green's functions for the induced strain field of a ring-shaped pressure source within an elastic half-space (Segall 1992) are made. The corrected Green's functions are used to obtain the strain induced by the pore fluid pressure distribution within a depleting disked-shaped reservoir. Consequently, a corrected permeability variation model compared to our previously published, time-independent solution for rate-dependent PI (Zhang and Mehrabian 2021a) is presented. Finally, a mechanistically rigorous formulation of the permeability modulus parameter that commonly appears in the pertinent literature is suggested. In addition to the in-house developed finite-difference solutions, the presented analytical solutions are verified against results from the finite-element simulation of the same problems using COMSOL® Multiphysics (2018). The obtained rate-dependent PI of the reservoir is controlled by four dimensionless parameters, namely, the dimensionless rock bulk modulus, the Biot-Willis effective stress coefficient, Poisson's ratio, and rock initial porosity. The pore fluid pressure solution is shown to asymptotically approach the corresponding flow-only solution for large values of the dimensionless rock bulk modulus. Parametric analysis of the solution suggests that the well productivity loss has a reverse relationship with the dimensionless bulk modulus and initial porosity of the rock, whereas a direct relationship is identified with Biot-Willis effective stress coefficient and Poisson's ratio. Compared to the reservoir with a constant-pressure outer boundary, the PI of a reservoir with a no-flow condition at the outer boundary is shown to be more significantly hindered by the stress sensitivity of the reservoir rock.
Abstract Data Science is the current gold rush. While many industries have benefitted from applications of data science, including machine learning and Artificial Intelligence (AI), the applications in upstream oil and gas are still somewhat limited. Some examples of applications of AI include seismic interpretations, facility optimization, and data driven modeling – forecasting. While still naïve, we will explore cases where data science can be used in the day to day field optimization and development. The Midway Sunset (MWSS) field in San Joaquin Valley, California has over 100 years of history. The field was discovered in 1901 and had limited development through the 1960s. Since the start of thermal stimulation in 1964, the field has seen phased thermal flooding and cyclic stimulation. Recently there has been an increase in heat mining vertical and horizontal wells to tap the remaining hot oil. As with any brownfield, the sweet spots are long gone. Effort is now to optimize the field development and tap by-passed oil, thereby increasing recovery. The current operational focus includes field wide holistic review of remaining resource potential. Resources in the MWSS reservoirs are produced by cyclic steam method. Cyclic thermal stimulation has been effective as an overall depletion process and for stimulating the near wellbore region to increase production. It is imperative to properly identify target wells and sands for cyclic stimulation. Cyclic steaming in depleted zones or cold reservoirs is often uneconomical. The benefit comes when we can identify and stimulate only the warm oil. Identification of warm oil and short listing the wells for cyclic stimulation is a labor-intensive process. The volume of data can get so large that it may not be feasible for a professional to effectively do the analysis. In this paper, we present a case study of data analytics for high grading wells for cyclic stimulation. This method utilizes the machine power to integrate reservoir, and production data to identify and rank wells for cyclic stimulation and potentially increase success rate by minimizing suboptimal cyclic candidates.
Abstract Low salinity waterflooding has been an area of great interest for researchers for almost over three decades for its perceived "simplicity," cost-effectiveness, and the potential benefits it offers over the other enhanced oil recovery (EOR) techniques. There have been numerous laboratory studies to study the effect of injection water salinity on oil recovery, but there are only a few cases reported worldwide where low salinity water flooding (LSW) has been implemented on a field scale. In this paper, we have summarized the results of our analyses for some of those successful field cases for both sandstone and carbonate reservoirs. Most field cases of LSW worldwide are in sandstone reservoirs. Although there have been a lot of experimental studies on the effect of water salinity on recovery in carbonate reservoirs, only a few cases of field-scale implementation have been reported for the LSW in carbonate reservoirs. The incremental improvement expected from the LSW depends on various factors like the brine composition (injection and formation water), oil composition, pressure, temperature, and rock mineralogy. Therefore, all these factors should be considered, together with some specially designed fit-for-purpose experimental studies need to be performed before implementing the LSW on a field scale. The evidence of the positive effect of LSW at the field scale has mostly been observed from near well-bore well tests and inter-well tests. However, there are a few cases such Powder River Basin in the USA and Bastrykskoye field in Russia, where the operators had unintentionally injected less saline water in the past and were pleasantly surprised when the analyses of the historical data seemed to attribute the enhanced oil recovery due to the lower salinity of the injected water. We have critically analyzed all the major field cases of LSW. Our paper highlights some of the key factors that worked well in the field, which showed a positive impact of LSW and a comparative assessment of the incremental recovery realized from the reservoir visa-a-vis the expectations generated from the laboratory-based experimental studies. It is envisaged that such a comparison could be more meaningful and reliable. Also, it identifies the likely uncertainties (and their sources) associated during the field implementation of LSW.