Formation damage caused by drilling-fluid invasion, production, or injection can lead to positive skin factors and affect fluid flow by reducing permeability. When mud filtrate invades the formation surrounding a borehole, it will generally remain in the formation even after the well is cased and perforated. This mud filtrate in the formation reduces the effective permeability to hydrocarbons near the wellbore. It may also cause clays in the formation to swell, reducing the absolute permeability of the formation. In addition, solid particles from the mud may enter the formation and reduce permeability at the formation face.

Fracture diagnostic techniques are divided into several groups.[1] Direct far-field methods consists of tiltmeter-fracture-mapping and microseismic-fracture-mapping techniques. These techniques require sophisticated instrumentation embedded in boreholes surrounding the well to be fracture treated. When a hydraulic fracture is created, the expansion of the fracture causes the earth around the fracture to deform. Tiltmeters can be used to measure the deformation and to compute the approximate direction and size of the created fracture.

The most important data for designing a fracture treatment are the in-situ stress profile, formation permeability, fluid-loss characteristics, total fluid volume pumped, propping agent type and amount, pad volume, fracture-fluid viscosity, injection rate, and formation modulus. It is very important to quantify the in-situ stress profile and the permeability profile of the zone to be stimulated, plus the layers of rock above and below the target zone that will influence fracture height growth. There is a structured method that should be followed to design, optimize, execute, evaluate, and reoptimize the fracture treatments in any reservoir. The first step is always the construction of a complete and accurate data set. Table 1 lists the sources for the data required to run fracture propagation and reservoir models.

Mini frac, or diagnostic fracture injection test (DFIT), is a short hydraulic fracturing test that provides formation break-down pressure, minimum horizontal stress, and reliable value for formation permeability of shale reservoirs. The calculated formation permeability is particularly more reliable from the analysis of the second cycle of the mini frac test because the fracture is already created. In this paper, we present a simple technique to analyze and interpret DFIT data similar to the analysis of the classic drillstem test (DST) data in vertical wells. The only difference is that in DFIT pressure-time characteristics approximate the linear flow regime while in DST, pressure-time behavior follows the radial flow regime.

In general, DFIT analysis provides matrix permeability while the analysis of long-term pressure decline of the production data yields stimulated formation permeability, which can be attributed to microfracture permeability. Thus, we will show how we utilize the permeability from DFIT, and the permeability calculated from the production decline data of production wells (i.e., multistage hydraulically fractured wells) to construct a viable dual-porosity model to assess the performance of wells under primary production and gas injection EOR. We also compare our results with those of Nolte G-function.

The paper includes numerical modeling of two-phase nonlinear flow, analytical solution methodology from multi-phase flow, experimental data, and field data to illustrate the viability of our interpretation method. Furthermore, our analysis technique is simple because it only uses pressure falloff data points during the shut-in period of the DFIT. Not only the method is confirmed by numerical modeling, but it is also verified by pressure falloff from laboratory data.

Application of horizontal wells and hydraulic fracturing achieves commercial productivity of unconventional oil and gas resources. Complex fracture networks (CFN) provide flow channels and significantly affect well performance in unconventional reservoirs. However, traditional rate transient analysis (RTA) models barely consider the effect of CFN on production performance. Besides, the impact of multi-phase flow on rate transient behaviors is still unclear. Neglecting these crucial effects could cause incorrect rate transient response and erroneous estimation of well and fracture parameters.

To fill this gap, this paper investigates the multi-phase rate transient behaviors considering CFN, and tries to investigate in what situations the multi-phase model should be used to obtain more accurate results. Firstly, an embedded discrete fracture model (EDFM) is generated instead of local grid refinement (LGR) method to overcome the time-intensive computation performance. The model is then coupled with reservoir models using non-neighboring connections (NNCs). Secondly, eight cases are designed using the EDFM technology to analyze the effect of natural fractures, formation permeability, and relative permeability on rate transient behaviors. Thirdly, Blasingame plot, log-log plot, and linear flow plot are used to analyze the differences of rate transient response between single-phase and multi-phase flow in reservoirs with CFN. For multi-phase flow, severe deviations can be observed on RTA diagnostic plots compared with single-phase model. The combination of three kinds of RTA type curves can obviously characterize the differences from early to late flow regimes and improve the interpretation accuracy as well as reduce the non-unicity. Finally, field application in Permian Basin demonstrates that multi-phase RTA model and type curves are required for analyzing the production and pressure data since single-phase RTA analysis will lead to big errors for interpretation results.

Diagnostic fracture injection tests (DFITs) have been widely studied and implemented in unconventional reservoirs to derive properties such as closure stress, pore pressure, and permeability. During a DFIT, a small volume of water is pumped into a formation to create a small-sized crack. Formation permeability is typically obtained by means of modeling fluid leakoff during the shut-in period. Early studies have assumed a constant fluid pressure boundary condition on the fracture walls or a constant leakoff rate into the formation. However, the results deduced based on these assumptions may introduce significant errors because the fluid pressure inside a fracture dissipates quickly as the fluid leaks off into the formation. In this study, we propose a material balance approach to obtain formation permeability using DFIT data. The proposed analysis takes into account fluid leakoff during both fracture propagation and well shut-in periods. To model fluid leakoff during fracture propagation, we adopt the superposition principle to decompose the problem into two separate problems; we then obtain the analytical solution. Two synthetic cases are presented to validate the proposed analysis. The results suggest that the proposed approach provides a good estimation of formation permeability. This approach has broad field application potential, as it can be used even when pressure data contains significant levels of noise. In addition, the solution is more accurate than those provided in available studies of formation permeability estimation using DFITs data, especially when formation permeability is not extremely tight.

Completion designs in horizontal, multi-stage wellbores is a complex problem and has many potential scenarios depending on the desired well performance. Up until recently the industry has been focused on improving early-time productivity; 90-day cumulative production, 24-hour initial potential (IP) or achieving a target daily rate. Many operators are now refocusing on a desired economic outcome; achieving free cash flow, improving return on investment and increased net present value. Our paper will discuss the effects that different completion design changes have on these desired results and present examples of past well performance and new well performance utilizing these changes.

Our methodology will include the analysis of producing well histories to determine average reservoir permeability and completion effectiveness as described by the number of created, producing transverse fractures and the average effective length of those fractures. From this characterization of the reservoir and past completions we will forward model completion design scenarios to determine the effect changes in design have on the performance parameters we wish to achieve. The process used includes hundreds of completion design scenarios which are compared to each other to find the design which maximizes the desired economic result. Multiple examples of this will be presented.

Increasing early-time production performance has resulted in increasing capital expenditure. Several completion design parameters lead to increases in this performance parameter (IP); increasing lateral length, increasing stage volume and decreasing stage spacing. However, all of these changes increase the capital spent on the completion. To achieve improved economic performance parameters, balancing the total capital expenditure against the revenue generated becomes the primary focus. What may have worked in the past to meet past goals does not work to meet new, economic focused goals. The most important parameter we must know when designing the completion is the reservoir permeability. This value determines the reservoirs ability to deliver hydrocarbons to the well and how effective the reservoir can be to creating the revenue necessary to pay for the capital spent. Identifying the balance between capital spent and revenue generated leads to completion designs that reduce well cost while maximizing economic parameters versus maximizing early-time production alone.

The results and conclusions from this paper will run contrary to the industry's past trends in completion design. However, the focus on economic improvement can be a pathway to our industry to achieve better economic results. Our industry has provided increased energy independence for our nation, but far too many companies are suffering financially while doing so. Billions of barrels of oil have been produced, yet many are going broke.

Since 2010, hydrocarbon production from long horizontal wells targeting shales has become the norm for industry leaders. Because of the steep decline rates, it is vital to understand the reservoir and its properties before going through with a full-scale fracture stimulation. Through the application of Diagnostic Fracture Injection Tests (DFIT), one can determine accurate estimates of closure pressure, net pressure, pore pressure, formation permeability, and induced fracture geometry. The Utica shale is among the most promising reservoirs of the future, but there is limited information available discussing its properties. In SPE-196149-MS, we analyzed a DFIT from one horizontal well targeting the Utica. However, in order to fully understand the Utica shale at scale, further analysis is required. In this study, we will present three additional horizontal wells targeting the Utica, and analyze the pressure and its derivative to accurately estimate the properties mentioned above.

DFIT analysis is an advanced technique to accurately predict stress regimes and reservoir properties. However, interpretation of DFIT data is challenging, especially in shale formations. In this study, we overview the geologic properties of the Utica shale, discuss the development of DFIT analysis and its governing equations, then present the three data sets and resulting conclusions. We specifically discuss the Tangent Line Method, the Compliance Method, and the Variable Compliance method in detail, while comparing their underlying equations and assumptions to determine closure pressure. After-Closure analysis is then performed in order to verify fracture closure and identify flow regimes. Through linear regression of this data, pore pressure from a linear flow regime is extrapolated, and through numerical simulation, key reservoir properties, such as permeability and fracture geometry, are estimated for the Utica shale.

The DFIT interpretation and simulation results from this study are very insightful. Interpreting the GdP/dG function, the closure pressure ranges from 4,943 psi to 6,141 psi, contributing to a closure pressure gradient of 0.797 to 0.891 psi/ft for the Utica shale. Based on the pressure transient analysis, the pore pressure ranges from 3,238 psi to 4,064 psi, contributing to a pore pressure gradient of 0.486 to 0.616 psi/ft for the Utica shale. Additionally, field wide ranges of reservoirs properties are presented, allowing industry to further optimize their drilling and fracing techniques in the Utica shale. Two of the wells in this study are close in proximity and show very similar results both in After-Closure analysis and in pressure response curves. The third well displays a different GdP/dG response, leak off characteristics, pressure transient behaviors, formation permeability, and fracture geometry. This variance in results can be attributed to regional differences in geology, stresses, and pressures. Therefore, operators need to consider regional differences in reservoir properties in order to enhance the development of Utica shale and unlock all potential recoverable hydrocarbons.

ABSTRACT Unconventional hydrocarbon reservoirs are deposits that do not produce economically without the assistance of massive stimulation or the application of special recovery technologies. Hydraulic fracturing is a stimulation technique that aims to increase the well productivity by establishing highly conductive structures (fractures). This paper presents a sensitivity study to evaluate the influence of some variables of hydraulic fracturing on well productivity. In this work, a design of experiments (DOE) was performed based on the Latin Hypercube samplings. This study serves as a basis for the computational tool for the analysis of hydraulic fracturing of gas wells in unconventional fields. This tool is coupled in an optimization module based on evolutionary algorithms that aims to optimize well production from hydraulic fracturing parameters, minimizing also the cost. INTRODUCTION Petroleum has represented an important part of the world's energy. Unconventional reservoirs were responsible for a revolution in the volume and profile of gas production in USA and throughout the world, the unconventional deposits are gaining great exposure. The shale formations and other tight formations as sandstone and carbonate formations are composed of fine-grained particles with pores on the nanometer scale. Thus, these reservoirs do not produce economically without the assistance of massive stimulation or the application of special recovery technologies (Holditch, 2001). Therefore, to make production feasible in these fields, a productivity analysis must be performed to find the best configuration for hydraulic fracturing design that maximizes productivity. Some studies have been reported to use optimization procedure coupled with a fracturepropagation model to optimize design parameters leading to maximum net present value. Hareland et al. (1993) use a three-dimensional hydraulic fracturing model in conjunction with a fractured reservoir production model to optimize hydraulic fracture design. The fracturing parameters optimized in this paper are fracture length and fracturing fluid pump rate. As a result, they got the NPV for some discrete values of fracture length and pump rate. Rueda et al. (1994) propose an optimization method based on Mixed Integer Linear Programming (MILP). In this work the objective function is the discounted net present value due to a fracture treatment, optimizing fracturing variables.

Logs provide the most economical and complete source of data for evaluating layered, complex, low porosity, tight gas reservoirs. All openhole logging data should be preprocessed before the data are used in any detailed computations. Once the data have been preprocessed and stored in a digital database, a series of statistical analyses must be conducted to quantify certain evaluation parameters. The series of articles by Hunt et al.[3] clearly describes the steps required to: To correctly compute porosity in tight, shaly (clay-rich) reservoirs, one of the first values to compute is the volume of clay in the rock. The clay volume is normally computed using either the self-potential (SP) or the GR log readings.