Engineered Shale Completions Based On Common Drilling Data

Abstract

Current best practices in North America's shale basins deliver inconsistent production results because of a lack of reservoir understanding along the lateral. Reservoir data is difficult and expensive to obtain in horizontal sections; therefore, most completions are designed geometrically, with little or no concern for reservoir heterogeneity. This leads to dramatic variability in productivity between neighboring wells, often described as the statistical nature of the play. In this study, we demonstrate a reliable, cost effective methodology that empowers shale operators with reservoir data on every well. This technique will enable operators to engineer their designs by using reservoir data on every completion, resulting in significant productivity improvement without any significant increased cost or inconvenience. This methodology leverages commonly available drilling measurements (rate of penetration, weight on bit, rotational speed, hole diameter, flow rate, differential pressure and standpipe pressure) and mud motor parameters (Kn, Tmax, ΔPmax) to derive Mechanical Specific Energy (MSE), using well established algorithms. The MSE parameter is then shown to be a good proxy for Unconfined Compressive Strength, a valuable reservoir parameter commonly used in frac designs. The MSE drives a facies-based answer product that enables the operator to position perforation clusters so that they breakdown at a common treating pressure, resulting in uniform fracture treatment within each frac stage. To date the technique has been applied successfully on over 60 wells. It is validated through comparison to openhole wireline logs and by comparing productivity to offsetting wells. The technique is shown to be superior to similar attempts done by using openhole wireline logs and production data confirms its accuracy and reliability. This study demonstrates that drilling data can be effectively used to derive reservoir parameters that lead to completion designs that produce superior production results. This methodology is the first application of drilling data to derive reservoir parameters that are then used to engineer a completion design. The significance of this development cannot be overstated, since it provides shale operators with the guidance they need to intelligently engineer their completions. The end result is significant productivity increases without any associated cost or inconvenience.

Confinement of laser plasma expansion with strong external magnetic field

“…E p is plasma energy calculated by initial plasma kinetic energy plus initial plasma internal energy in MHD code. In experiments, E p is the absorbed laser energy which is the product of incident laser energy and the laser energy deposition rate [12]. V max is set to be r 4 3 p in simulation.…”

“…But strong stimulated scattering process in underdense gas will depress the laser-target energy coupling efficiency [23]. Thus it is proposed [12] that the underdense gas can be replaced by intense external magnetic field, which can avoid the stimulated scattering process in underdense gas. Our experimental and simulation results have shown that the external magnetic field can effectively confine the wall plasma expansion ablated by laser beam.…”

“…Different from the approach of inhibiting electron heat loss [1][2][3][4][5][6][7][8][9][10][11], strong external magnetic field was also proposed [12] by Logan to replace the underdense gas filled in hohlraum and confine the wall plasma expansion recently. This magnetic field filled hohlraum further reduces the density of the underdense gas, which can also suppress the stimulated scattering process possibly.…”

Evaluation of Reservoir Quality and Forecasted Production Variability along a Multi-Fractured Horizontal Well. Part 2: Selected Stage Forecasting

“…The workflow consists of the following steps: (1) quantify reservoir quality (RQ) and completion quality (CQ) along a horizontal well, as determined from a combination of petrophysical, geochemical and geomechanical properties; (2) identify intervals of higher RQ/CQ that can be selected for stimulation; (3) use a simulation model, populated with properties determined from step 1, to generate production forecasts for all stages of the well (assuming a geometric fracturing approach); (4) compare the production forecast for the selected (higher RQ/CQ) intervals against the production forecast for all intervals (geometric fracturing case) to determine if fewer stages can be completed in the well without significant loss in well performance. Previous approaches for Steps 1 and 2 have been discussed in the literature e.g., [16][17][18][19][20][21]. If…”