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Nicholson, A. Kirby (Pressure Diagnostics Ltd.) | Bachman, Robert C. (Pressure Diagnostics Ltd.) | Scherz, R. Yvonne (Endeavor Energy Resources) | Hawkes, Robert V. (Cordax Evaluation Technologies Inc.)
Abstract Pressure and stage volume are the least expensive and most readily available data for diagnostic analysis of hydraulic fracturing operations. Case history data from the Midland Basin is used to demonstrate how high-quality, time-synchronized pressure measurements at a treatment and an offsetting shut-in producing well can provide the necessary input to calculate fracture geometries at both wells and estimate perforation cluster efficiency at the treatment well. No special wellbore monitoring equipment is required. In summary, the methods outlined in this paper quantifies fracture geometries as compared to the more general observations of Daneshy (2020) and Haustveit et al. (2020). Pressures collected in Diagnostic Fracture Injection Tests (DFITs), select toe-stage full-scale fracture treatments, and offset observation wells are used to demonstrate a simple workflow. The pressure data combined with Volume to First Response (Vfr) at the observation well is used to create a geometry model of fracture length, width, and height estimates at the treatment well as illustrated in Figure 1. The producing fracture length of the observation well is also determined. Pressure Transient Analysis (PTA) techniques, a Perkins-Kern-Nordgren (PKN) fracture propagation model and offset well Fracture Driven Interaction (FDI) pressures are used to quantify hydraulic fracture dimensions. The PTA-derived Farfield Fracture Extension Pressure, FFEP, concept was introduced in Nicholson et al. (2019) and is summarized in Appendix B of this paper. FFEP replaces Instantaneous Shut-In Pressure, ISIP, for use in net pressure calculations. FFEP is determined and utilized in both DFITs and full-scale fracture inter-stage fall-off data. The use of the Primary Pressure Derivative (PPD) to accurately identify FFEP simplifies and speeds up the analysis, allowing for real time treatment decisions. This new technique is called Rapid-PTA. Additionally, the plotted shape and gradient of the observation-well pressure response can identify whether FDI's are hydraulic or poroelastic before a fracture stage is completed and may be used to change stage volume on the fly. Figure 1: Fracture Geometry Model with FDI Pressure Matching Case studies are presented showing the full workflow required to generate the fracture geometry model. The component inputs for the model are presented including a toe-stage DFIT, inter-stage pressure fall-off, and the FDI pressure build-up. We discuss how to optimize these hydraulic fractures in hindsight (look-back) and what might have been done in real time during the completion operations given this workflow and field-ready advanced data-handling capability. Hydraulic fracturing operations can be optimized in real time using new Rapid-PTA techniques for high quality pressure data collected on treating and observation wells. This process opens the door for more advanced geometry modeling and for rapid design changes to save costs and improve well productivity and ultimate recovery.
Abstract If the interference effect is not considered for well test interpretation, it could lead to wrong analyses especially in boundary identification. In addition, there are some case where interference effects might be hidden where it may not be obvious due to data uncertainty. Therefore, special diagnostics of the multi well interference models will be required to differentiate between the boundary and interference effects. In addition, there is no analytical method for a well in a multi-well reservoir with no flow boundary condition. In this paper a new method was developed to model Pressure Analysis of Well-Test Data from a Well in a Multi-well reservoir with no flow boundary condition. It covers; Derivation of the analytical model, based on the superposition principle, with and without "no flow" boundary condition; Modeling of various combination of testing & interfering well cases (i.e. testing well is on production or shut-in while interfering well is on production or shut-in) Modeling of various combinations of testing & interfering well rate cases (i.e. production or injection, rate variations) Modeling of various number of interfering well cases (i.e. location and well count) Investigation deeply on how to differentiate between the boundary and interference effects (or vice versa) and developing the special diagnostics to able to detect interference effect directly. Results are shown that 1) the multi-well interference effect with and without no flow boundary condition has huge impact on the well test interpretation and this effect might be interpreted as a boundary effect if interference is not considered. 2) Build-up (BU) behavior of testing well is depending on whether interfering well is shut-in or producing. If interfering well is producing, pressure derivative of BU curve is concave down and If the interfering wells are shutting in, pressure derivative of BU curve is concave up 3) the interfering well rate is affecting magnitude of impact on pressure derivative and the higher the rates, the bigger the response 4) the interfering well distance is affecting the timing of deviation on pressure derivative and the closer the distance, the quicker the response Study also concluded that there are 3 special characteristics, which only exists in interference cases, and which does not exist in boundary cases. Therefore, those characteristics can be used to differentiate between the interference and boundary effects. Those are 1) Pressure decrease or rise at the beginning of well testing 2) the drawn-down (DD) and BU pressure derivatives in Log-Log plot are different (i.e. when BU is concave up, DD is concave down or vice versa) in case of interfering well is on continuous production 3) The consecutive BU's (or DD's) pressure derivatives on Log-Log plot are different and changing over time
An operator has designed a demonstration project for carbon dioxide (CO2) enhanced oil recovery (EOR) and has implemented it in one of its fields. The main objectives of the demonstration project are estimation of sequestered CO2, determination of incremental oil recovery, and evaluating the risks and uncertainties involved, including migration of CO2 within the reservoir and operational concerns. It is estimated that approximately 40% of the injected CO2 will be sequestered permanently in the reservoir. Given the relatively light nature of crude oils and generally high reservoir pressures in Saudi Arabia, CO2 injection is a viable recovery method, especially in flooded reservoirs. An initial screening highlighted several good candidates for CO2 injection.
Monitoring the waterflooding oil-recovery process is a difficult task for seismic-based methods in hard carbonate reservoirs. The changes in velocity and density caused by water/oil substitution are too small when compared with the errors involved in repeating the measurements. The authors detail the development of a technique based on surface-to-borehole controlled-source electromagnetics (CSEM), which exploits the large contrast in resistivity between injected water and oil to derive 3D resistivity distributions, proportional to saturations, in the reservoir. CSEM techniques for reservoir-fluid characterization and monitoring are applied on a commercial basis for cross-well configurations. The method is based on electromagnetic (EM) induction and, as such, uses magnetic sources and magnetic receivers.
In the Permian Wolfcamp shale formation in west Texas, density fields of microseismic events were mapped in four dimensions and variations were noted in the geometry of the hydraulic stimulation as well as in the development of pressure away from the perforations. In addition to aiding well-spacing decisions, these data were used to study individual-well geometries and compare variations in the microseismic response between adjacent wells. The data sets demonstrate that high-fidelity microseismic data can be acquired by use of downhole tractored and multiobservational well-imaging techniques to understand stimulations and the stress fields better as indicated by microseismic data. The data are called high-fidelity because, in general, they are excellent data that are consistent and conform to standard understandings of stimulations. Beyond the robustness in event counts, the data typically have a high signal/noise ratio with high-quality waveforms for picking and consistent hodograms across the tools within the array.
Before the giant Johan Sverdrup field had produced even one barrel of oil, operator Equinor and its license partners set a recovery ambition of greater than 70% for the field. These include the field size and reservoir characteristics, early assessments and investments for improved oil recovery (IOR), data acquisition, reservoir monitoring, and digitalization. With a recoverable volume range of 2.2–3.2 billion BOE, Johan Sverdrup is a giant oil field approximately 150 km west of Stavanger, the third-largest oil field on the Norwegian Continental Shelf (NCS). The first phase of the field came on stream in October 2019. A predrilling campaign included eight oil producers and 12 injectors.
A normal five-spot polymer-flooding pilot has been conducted at the Mangala field, one of the largest onshore fields in India, and results are encouraging in terms of additional oil recovery and reduction of water cut. Polymer flooding has the potential to improve sweep efficiency in the field significantly and to increase expected ultimate recovery (EUR). The project is one of the largest in the world in terms of scale, polymer usage, and related facility and logistics. The main reservoir unit in Mangala field is the Fatehgarh group. Five reservoir units (FM1–FM5) have been named from the top down.
In highly fractured carbonate reservoirs, the conventional method of monitoring oil-rim movement is running wireline gradiometric surveys periodically. However, some operators have found that this method is inconclusive and is unable to provide information in a manner timely enough to influence operations because the gradiometric surveys are only run a few times a year. In this paper, the authors describe a project to design, field trial, and qualify an alternative solution for real-time monitoring of the oil rim in carbonate reservoirs that overcomes these disadvantages. The methodology of performing gradiometric surveys can be applied in reservoirs successfully where the permeability of the formation is high and where the formation is fractured such that good communication exists between fluid within the formation and within the observation well (given that the well casing is highly perforated across the full length of the reservoir section). Under these conditions, the fluid levels measured by the gradiometric surveys give the operator enough information about the oil rim within the reservoir to adopt an active smart-field production method.