The SPE has split the former "Management & Information" technical discipline into two new technical discplines:
- Data Science & Engineering Analytics
The SPE has split the former "Management & Information" technical discipline into two new technical discplines:
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Abstract Existing fracture diagnostic methods such as micro-seismic monitoring and tiltmeters do not provide information about fracture connectivity to the wellbore. In this work, we present a chemical tracer flowback based fracture diagnostic method to (a) estimate the fraction of the created fracture area which is open and connected to the wellbore, and (b) understand the effect of induced un-propped (IU) fracture closure on the tracer response. We conducted a reservoir simulation study to model tracer injection and flowback in a complex fracture network with the help of an effective model. The model captures the effect of fracture opening and closure due to changes in the in-situ effective stress during flowback. As the fracture pressure is reduced, fractures close over time. This directly affects the tracer response during flowback. The impact of the closure rate of induced unpropped (IU) fractures on tracer response was demonstrated through simulation results. Fracture length and permeability were lumped to define an effective connected fracture length, a parameter which correlates with production. Neural network based inverse modeling was performed to estimate effective connected fracture length using tracer data. Simulation results indicate that the tracer response is dominated by the fractures which are open and connected to the wellbore. Multiple peaks in the tracer response curves can be explained by the closure of IU fractures. Fracture closure can also explain the low tracer recovery typically observed in field tests. Tracer recovery is found to be proportional to production. Based on these observations, tracer peaks and recovery parameters were selected for training the neural network for inverse modeling. The trained neural network was used to estimate the effective connected fracture length. We observed a good match between neural network prediction and the fracture parameters in the simulation. We present a new method to analyze chemical tracer data which includes the effect of flow and geomechanics on tracer flowback. The proposed approach can help in estimating the degree of connectivity between the wellbore and open connected fractures.
Abstract Chemical tracer is an alternative technique for hydraulic fracture diagnosis other than tiltmeter and microseismic mapping. Fracture volume is an essential parameter for stimulation optimization and production forecast. In our previous work, we proposed a simple, cost-effective method to assess the fracture volume using partitioning chemical tracer. In the hydraulic fracturing stage, a partitioning chemical tracer slug is injected along with the fracking fluid. In the created hydraulic fracture, the tracer partitions in both vapor and liquid phases and flow back in the production stage. By analyzing the tracer production data, we could estimate fracture volume and leak-off volume. This work will first investigate chemical tracer selection criteria for the purpose of fracture volume diagnosis. Tracer partition coefficient and tracer adsorption are the main considerations. Our results suggest a careful section is needed for partition coefficient, balancing the estimation accuracy and investigation area. In addition, the selected tracer should have negligible adsorption. Numerical simulation is another way to interpret tracer test. In the second part of this paper, we propose a modified Random Walk Particle Tracking (RWPT) algorithm to simulate the partitioning chemical tracer transport with multiple mobile phases. Output obtained through the RWPT is identical with analytical solution and its tracer critical breakthrough time is more accurate than the result from the finite-difference based simulations.
Summary Hydraulic fracturing is performed to enable production from low-permeability and organic-rich shale-oil/gas reservoirs by stimulating the rock to increase its permeability. Characterization and imaging of hydraulically induced fractures is critical for accurate prediction of production and of the stimulated reservoir volume (SRV). Recorded tracer concentrations during flowback and historical production data can reveal important information about fracture and matrix properties, including fracture geometry, hydraulic conductivity, and natural-fracture density. However, the complexity and uncertainty in fracture and reservoir descriptions, coupled with data limitations, complicate the estimation of these properties. In this paper, tracer-test and production data are used for dynamic characterization of important parameters of hydraulically fractured reservoirs, including matrix permeability and porosity, planar-fracture half-length and hydraulic conductivity, discrete-fracture-network (DFN) density and conductivity, and fracture-closing (conductivity-decline) rate during production. The ensemble Kalman filter (EnKF) is used to update uncertain model parameters by sequentially assimilating first the tracer-test data and then the production data. The results indicate that the tracer-test and production data have complementary information for estimating fracture half-length and conductivity, with the former being more sensitive to hydraulic conductivity and the latter being more affected by fracture half-length. For characterization of DFN, a stochastic representation is adopted and the parameters of the stochastic model along with matrix and hydraulic-fracture properties are updated. Numerical examples are presented to investigate the sensitivity of the observed production and tracer-test data to fracture and matrix properties and to evaluate the EnKF performance in estimating these parameters.
Abstract Tracer surveillance has long been established as a proven method in direct characterization of the dynamic heterogeneity, swept volume and remaining oil saturation estimation of the reservoir volume. It is the only surveillance technique where measurements are made at the scale of the entire reservoir volume contributing to flow. Such application of tracer surveillance has primarily been constrained to the conventional floods. The direct translation of the established interpretation methods for conventional tracer tests (interwell/single well tracer tests) is not possible for unconventional reservoirs. Such translation is limited because for unconventional reservoirs, huff and puff technique is used where boundary conditions during injection vs production are different. For an unconventional offset well the tracer flow is like a conventional producer (interwell tracer test). This work aims to showcase an analytical tracer interpretation model for unconventional tracer flow for huff and puff/injection wells. The analytical model estimates the swept volume associated with the injection well and the offset well while quantifying the dynamic heterogeneity of the swept volume as a Lorenz coefficient to help characterize flow through the fracture volume. The analytical tracer model developed for the unconventional wells is based on the Method of Moments (MoM), the same method used for conventional reservoir characterization. The MoM is independent of any flow regime (only fracture dominated flow, fracture and matrix flow or just matrix flow and any combination thereof) conceptually applied to flow in unconventional reservoirs. This is based on material balance applied over the entire control volume. This novel analytical method estimates the flowrate from the tracer swept part for an injection well during flow back and for the offset well(s) after tracer injection. The flowrate estimation from the tracer swept reservoir volume is critical for MoM based analytical method for evaluation of the swept volume and the dynamic heterogeneity characterizing flow. The analytical model was verified against numerical simulations with varying fracture and matrix flow characteristics. The calculated reservoir swept volumes were within 5% of the simulation reservoir swept volume. The analytical model also quantified dynamic heterogeneity in the form of a Lorenz coefficient which shows direct correlation with parameters that characterize fracture conductivity (aperture distribution, half length) in simulation models. The model has been successfully applied to field data for flow volume characterization. The new tracer interpretation model allows for EUR optimization because recovery from unconventional reservoirs is directly corelated with the stimulated rock volume, fracture conductivity and how it is distributed. This novel tracer analytical method provides ranges for the swept volume and dynamic heterogeneity which could be critical for completion design optimization (well spacing, stage spacing, frac cluster spacing etc.) as well as potential IOR/EOR optimization for unconventional wells. The dynamic heterogeneity also allows for the direct estimation of flow conformance and addressing such conformance issues might help to recover additional oil.
Abstract A single-well backflow tracer test to estimate reservoir wettability has been developed. This test consists of the injection of first brine containing tracers and then oil containing tracers. The tracers used are esters, similar to those used in the measurement of residual oil saturation, and nonreactive aqueous and oleic tracers for material balance control. The esters hydrolyze during a shut-in time and then the well is produced. The tracer production data, water cuts, and bottomhole pressures are all sensitive to wettability-dependent properties. The most important by far is relative permeability, but capillary pressure and mixing behavior (dispersion and capacitance) are also important. In the absence of fluid drift, reservoir properties such as permeability and porosity have little influence in the test results. From the results of the test, the preferential reservoir wettability may be directly inferred. By matching the test data using a compositional reservoir simulator, these properties can be estimated and used to infer wettability or simply used directly in subsequent simulations of the reservoir performance. We illustrate this process using a chemical flooding simulator. This is a reservoir simulator that includes the usual features of a finite-difference simulator in terms of reservoir description, plus compositional and chemical features needed for this application such as kinetics, partitioning, dispersion, and capacitance. Special care has been taken to ensure numerical accuracy. The CPU time on a CRAY Y-MP required for each simulation was: 10 seconds for a 1D-radial (31 blocks), 3 minutes for a 2D-areal (609 blocks), and 9 minutes for a 3D (1827 blocks) simulation, so many cases can be affordably studied. Introduction Wettability has been considered the most important factor that controls the location, distribution, and characteristics of the flow of fluids in a reservoir. Changes in the wetting conditions of the samples in core analysis have been shown to affect the transport properties of a reservoir such as relative permeability and capillary pressure, dispersion of tracers, waterflood behavior, tertiary recovery, irreducible water saturation, residual oil saturation, and electrical properties. Although wettability can be measured in the laboratory using cores, the wetting condition of the cores may be subject to error, mainly because of inadequate well coring fluid and handling techniques, problems with packing and preserving the cores, laboratory procedures for cleaning and preparing the samples, test temperature, and test fluid and test techniques. Thus, an in-situ method would obviously be of great advantage. The only in-situ method that we are aware of besides the one proposed here is that of Desbrandes and Bassiouni, who proposed using a wireline formation tester. Wettability impacts the economics of any recovery process because it affects the efficiency of the displacement of oil by water, alters the predicted breakthrough time and the required amounts of injected fluid to achieve a given reduction in oil saturation, is used to specify the kind of chemicals to be used in an enhanced recovery process, and affects the estimated reserves of the reservoir. Therefore, it is of great importance to know the reservoir wettability in order to predict the correct performance of the reservoir under study. An in-situ evaluation of reservoir wettability thus would be an attractive alternative not only because it is unaffected by extraneous fluids but it also would sample a much larger volume of the reservoir. The combined use of laboratory and well tests is the most likely to yield definitive results. P. 325^