|Theme||Visible||Selectable||Appearance||Zoom Range (now: 0)|
If least-squares linear regression is used to compute N in Step 5, an equation analogous to Eq. 17 is used (where Eow is substituted for Eowf). This solution method is iterative because the material-balance error must be minimized. This calculation is carried out with a trial-and-error method or a minimization algorithm. Least-squares linear regression and minimization algorithms have become standard features in commercial spreadsheets.
The process of drilling and completing coalbed methane (CBM) wells is similar to wells in conventional reservoirs. Coring, however, can pose special challenges. The first step in creating a drilling program for a CBM well involves gathering information about existing wells in a given area. After these data are gathered and analyzed, a preliminary drilling and completion prognosis can be drafted with the input of field operations personnel. An important aspect in drilling frontier or appraisal wells is to keep the drilling procedures relatively simple.
Horizontal wells are high-angle wells (with an inclination of generally greater than 85) drilled to enhance reservoir performance by placing a long wellbore section within the reservoir. Horizontal Well contrasts with an extended-reach well, which is a high-angle directional well drilled to intersect a target point. There was relatively little horizontal drilling activity before 1985. The Austin Chalk play is responsible for the boom in horizontal drilling activity in the U.S. Now, horizontal drilling is considered an effective reservoir-development tool. Horizontal wells are normally characterized by their buildup rates and are broadly classified into three groups that dictate the drilling and completion practices required, as shown in Table 1.
Introduction Tight gas is the term commonly used to refer to low permeability reservoirs that produce mainly dry natural gas. Many of the low permeability reservoirs that have been developed in the past are sandstone, but significant quantities of gas are also produced from low permeability carbonates, shales, and coal seams. Production of gas from coal seams is covered in a separate chapter in this handbook. In this chapter, production of gas from tight sandstones is the predominant theme. However, much of the same technology applies to tight carbonate and to gas shale reservoirs. Tight gas reservoirs have one thing in common--a vertical well drilled and completed in the tight gas reservoir must be successfully stimulated to produce at commercial gas flow rates and produce commercial gas volumes. Normally, a large hydraulic fracture treatment is required to produce gas economically. In some naturally fractured tight gas reservoirs, horizontal wells and/or multilateral wells can be ...
Tight gas reservoirs generate many difficult problems for geologists, engineers, and managers. Cumulative gas recovery (thus income) per well is limited because of low gas flow rates and low recovery efficiencies when compared to most high permeability wells. To make a marginal well into a commercial well, the engineer must increase the recovery efficiency by using optimal completion techniques and decrease the costs required to drill, complete, stimulate, and operate a tight gas well. To minimize the costs of drilling and completion, many managers want to reduce the amount of money spent to log wells and totally eliminate money spent on extras such as well testing. However, in these low-permeability layered systems, the engineers and geologists often need more data than is required to analyze high permeability reservoirs.
A directional well can be divided into three main sections--the surface hole, overburden section, and reservoir penetration. Different factors are involved at each stage within the overall constraints of optimum reservoir penetration. Most directional wells are drilled from multiwell installations, platforms, or drillsites. Minimizing the cost or environmental footprint requires that wells be spaced as closely as possible. It has been found that spacing on the order of 2 m (6 ft) can be achieved.
Abstract Drilling in deep high-pressure high-temperature (HPHT) abrasive sandstone pose significant challenges: low rate of penetration (ROP), bit wear, differential sticking, and wellbore instability issues. These issues are magnified when attempting to drill long laterals in the direction of minimum stress. This paper focuses on the use of Managed Pressure Drilling (MPD) and Artificial Intelligence (AI) analytics to improve ROP. MPD is normally used to help drilling in formations with narrow mud weight window, it achieves this by controlling the surface backpressure to keep the annular pressure in the wellbore above the pore pressure and below the fracture gradient. One key benefit of using MPD is that high mud weight is no longer required, since the Equivalent Circulating Density (ECD) is going to be managed to maintain the overbalance. An example of a well that was drilled using MPD solely for ROP improvement is presented in this paper. This well achieved almost double the ROP of a control well, which was drilled in the same formation with no MPD. Essentially most of the drilling parameters used, which include, pump rate, revolution per minute (RPM), weight on bit (WOB), and other drilling practices, are controlled by the people on the rig. Incorporating AI analytics in the equation, help minimizes human intervention and could achieve further improvement in ROP. After the ROP improvement observed while using MPD, both technologies were combined in a well drilling the same formation. An example is presented for the well drilled using both technologies.
Degenhardt, John J. (W. D. Von Gonten Laboratories) | Ali, Safdar (W. D. Von Gonten Laboratories) | Ali, Mansoor (W. D. Von Gonten Laboratories) | Chin, Brian (W. D. Von Gonten Laboratories) | Von Gonten, W. D. (W. D. Von Gonten Laboratories) | Peavey, Eric (Shell Fellow-UROC / Texas A&M University)
Abstract Many unconventional reservoirs exhibit a high level of vertical heterogeneity in terms of petrophysical and geo-mechanical properties. These properties often change on the scale of centimeters across rock types or bedding, and thus cannot be accurately measured by low-resolution petrophysical logs. Nonetheless, the distribution of these properties within a flow unit can significantly impact targeting, stimulation and production. In unconventional resource plays such as the Austin Chalk and Eagle Ford shale in south Texas, ash layers are the primary source of vertical heterogeneity throughout the reservoir. The ash layers tend to vary considerably in distribution, thickness and composition, but generally have the potential to significantly impact the economic recovery of hydrocarbons by closure of hydraulic fracture conduits via viscous creep and pinch-off. The identification and characterization of ash layers can be a time-consuming process that leads to wide variations in the interpretations that are made with regard to their presence and potential impact. We seek to use machine learning (ML) techniques to facilitate rapid and more consistent identification of ash layers and other pertinent geologic lithofacies. This paper involves high-resolution laboratory measurements of geophysical properties over whole core and analysis of such data using machine-learning techniques to build novel high-resolution facies models that can be used to make statistically meaningful predictions of facies characteristics in proximally remote wells where core or other physical is not available. Multiple core wells in the Austin Chalk/Eagle Ford shale play in Dimmitt County, Texas, USA were evaluated. Drill core was scanned at high sample rates (1 mm to 1 inch) using specialized equipment to acquire continuous high resolution petrophysical logs and the general modeling workflow involved pre-processing of high frequency sample rate data and classification training using feature selection and hyperparameter estimation. Evaluation of the resulting training classifiers using Receiver Operating Characteristics (ROC) determined that the blind test ROC result for ash layers was lower than those of the better constrained carbonate and high organic mudstone/wackestone data sets. From this it can be concluded that additional consideration must be given to the set of variables that govern the petrophysical and mechanical properties of ash layers prior to developing it as a classifier. Variability among ash layers is controlled by geologic factors that essentially change their compositional makeup, and consequently, their fundamental rock properties. As such, some proportion of them are likely to be misidentified as high clay mudstone/wackestone classifiers. Further refinement of such ash layer compositional variables is expected to improve ROC results for ash layers significantly.
Abstract Assessment of effective mechanical properties such as elastic properties and brittleness can be challenging in the presence of complex rock composition, pore structure, and spatial distribution of minerals, especially in the absence of acoustic measurements. Conventional methods such as effective medium modeling, are not reliable for assessments of mechanical properties in complex formations such as carbonates, because solid skeleton of carbonates does not consist of granular minerals with ideal shapes. The effective medium models also overlook both the spatial distribution of petrophysical properties, and the coupled hydraulic and mechanical (HM) processes, which causes significant uncertainties in geomechanical evaluations. The objective of this paper is to develop a numerical method to enhance assessment of effective mechanical properties of anisotropic and heterogenous carbonate formations by modeling the variation of effective stress and the evolution of corresponding strain. The developed method takes into account the coupled HM processes, the realistic spatial distribution of rock inclusions (i.e., rock fabrics), dynamic fluid flow, pore pressure, and pore structure. To achieve this objective, we develop a pore-scale numerical simulator by satisfying conservation equations and considering the coupling among relevant HM phenomena. We adopt peridynamic theory to discretize the micro-scale medium. The inputs to our numerical modeling include pore-scale images of rock samples as well as mechanical and hydraulic properties of each rock inclusion. We perform image processing on micro-CT scan images of rock samples to obtain a realistic micro-scale structure of both rock matrix (i.e., concentration, spatial distribution, and shape of rock constituents) and pore space. We then assign realistic mechanical and hydraulic properties to each rock constituent within the pore-scale medium. The outcomes of numerical modeling include the variation of effective stress and the evolution of corresponding strain by honoring the variability in mechanical/hydraulic properties of rock inclusions caused by their spatial distribution, pore pressure, pore structure, natural fractures, and dynamic fluid flow at the micro-scale domain. We then compare the outcomes of numerical models with the mechanical properties estimated based on effective medium models.
Brinkley, Kourtney (Devon Energy) | Ingle, Trevor (Devon Energy) | Haffener, Jackson (Devon Energy) | Chapman, Philip (Devon Energy) | Baker, Scott (Devon Energy) | Hart, Eric (Devon Energy) | Haustveit, Kyle (Devon Energy) | Roberts, Jon (Devon Energy)
Abstract This case study details the use of Sealed Wellbore Pressure Monitoring (SWPM) to improve the characterization of fracture geometry and propagation during stimulation of inter-connected stacked pay in the South Texas Eagle Ford Shale. The SWPM workflow utilizes surface pressure gauges to detect hydraulically induced fracture arrivals athorizontal monitor locations adjacent to the stimulated wellbore (Haustveit et al. 2020). A stacked and staggered development in Dewitt County provided the opportunity to jointly evaluateprimary completion and recompletion efforts spanning three reservoir target intervals. Fivemonitor wells at varying distances across the unit were employed for SWPM during the stimulation of four wells. An operational overview, analysis of techniques, correlation with seismic attributes, image log interpretations, and fracture model calibration are provided. Outputs from this workflow allow for a refined analysis ofthe overall completion strategy. The high-density, five well monitor array recorded a total of 160 fracture arrivals at varying vertical and lateral distances, with far-field fracture arrivalsprovidingsignificant insight into propagation rates and geometry. Apronounced trend occurred in both arrival frequency and volumes pumped as monitor locations increased in distance from the treatment well. Specific to target zone isolation, it was identified that traversing vertically in section through a high stress interval yielded a 30% reduction inarrival frequency. An indirect relationship between horizontal distance and arrival frequency was also observed when monitoring from the same interval. A decrease in fracture arrivals from 70% down to 8% was realized as offset distance increased from 120 to 1,700 ft. The results from this study have proven to be instrumental in guiding interdisciplinary discussion. Assessing fracture geometry and propagation during stimulation, particularly in the co-development of a stacked pay reservoir, is paramount to the determination of proper completion volume, perforation design, and well spacing. Leveraging the observations of SWPM ultimately provides greater confidence in field development strategy and economic optimization.