This article focuses on interpretation of well test data from wells completed in naturally fractured reservoirs. Because of the presence of two distinct types of porous media, the assumption of homogeneous behavior is no longer valid in naturally fractured reservoirs. This article discusses two naturally fractured reservoir models, the physics governing fluid flow in these reservoirs and semilog and type curve analysis techniques for well tests in these reservoirs. Naturally fractured reservoirs are characterized by the presence of two distinct types of porous media: matrix and fracture. Because of the different fluid storage and conductivity characteristics of the matrix and fractures, these reservoirs often are called dual-porosity reservoirs.

Many wells, particularly gas wells in low-permeability formations require hydraulic fracturing to be commercially viable. Interpretation of pressure-transient data in hydraulically fractured wells is important for evaluating the success of fracture treatments and predicting the future performance of fractured wells. This page includes graphical techniques for analyzing post-fracture pressure transient tests after identifying several flow patterns that are characteristic of hydraulically fractured wells. Often, identification of specific flow patterns can aid in well test analysis. Five distinct flow patterns (Figure 1) occur in the fracture and formation around a hydraulically fractured well.[1]

Productivity estimates in horizontal wells are subject to more uncertainty than comparable estimates in vertical wells. Further, it is much more difficult to interpret well test data because of 3D flow geometry. The radial symmetry usually present in a vertical well does not exist. Several flow regimes can potentially occur and need to be considered in analyzing test data from horizontal wells. Wellbore storage effects can be much more significant and partial penetration and end effects commonly complicate interpretation. In vertical wells, variables such as average permeability, net vertical thickness, and skin are used. Horizontal wells need more detail. Not only is vertical thickness important, but the horizontal dimensions of the reservoir, relative to the horizontal wellbore, need to be known. Evaluation of data from a vertical wellbore will generally center on a single flow regime, such as infinite-acting radial flow, known as the MTR. However, a pressure-transient test in a horizontal well can involve as many as five major and distinct regimes that need to be identified. These regimes may or may not occur in a given test and may or may not be obscured by wellbore storage effects. Each flow regime can be modeled by an equation that can be used to estimate important reservoir properties.

The main reason for testing an exploration well is to take a fluid sample. Further reasons are to measure the initial pressure, estimate a minimum reservoir volume, evaluate the well permeability and skin effect, and identify heterogeneities and boundaries. Testing producing wells aims at verifying permeability and skin effect, identifying fluid behavior, estimating the average reservoir pressure, confirming heterogeneities and boundaries, and assessing hydraulic connectivity. We create a step change in rate--for instance, by closing a flowing well or an injection well (buildup or falloff, respectively); by opening a well previously shut in (drawdown); or by injecting in a well previously closed (injection). This rate change creates a change in pressure in the same well (exploration or production testing) or in a different well (interference testing).

As shale reservoirs have become more challenging, understanding well performance has become more important. Understanding well performance requires understanding the methods and limitations associated with the analysis and interpretation of both pressure and production data. Transient well analysis includes both traditional pressure transient analysis and production data analysis. The key to transient analysis is understanding how to recognize key flow regimes from their characteristic trends in the transient data. In this article we discuss the implementation of log-log diagnostic plots in the form of rate-normalized pressure (RNP) plots and the added value of buildup information when used to analyze, describe, and understand shale well performance.

As shale reservoirs have become more challenging, understanding well performance has become more important. Understanding well performance requires understanding the methods and limitations associated with the analysis and interpretation of both pressure and production data. Transient well analysis includes both traditional pressure transient analysis and production data analysis. The key to transient analysis is understanding how to recognize key flow regimes from their characteristic trends in the transient data. In this article we discuss the implementation of log-log diagnostic plots in the form of rate-normalized pressure (RNP) plots and the added value of buildup information when used to analyze, describe, and understand shale well performance.

Decline-curve analysis (DCA) is arguably the most commonly used method for forecasting reserves in unconventional reservoirs. Production data from several US unconventional plays are analyzed, and production forecasting is carried out with the traditional Arps methods as a basis for comparison. The results are compared with analytical models developed for each play to determine the suitability of each DCA method. At the most fundamental level, DCA involves fitting an empirical model of the trend in production decline from a well's history and projecting the trend into the future to determine the well's economic life and forecast cumulative production. The Arps decline model is established from the empirical observation that the loss ratio (the rate of change of the reciprocal of the instantaneous decline rate) is constant with time.

The understanding of solid-particle flow regimes in pipelines for any operational conditions is important for identifying the nature of particle interaction and movement. In this paper, a comprehensive analysis of solid-particle flow regimes in stratified flow in a horizontal pipeline is presented.

Abstract The first objective of this work is to determine the volume of hydrocarbon that can be moved from Resources other than Reserves (ROTR) to Reserves, or from Proved Undeveloped Reserves (PUD) to Reserves based on well placement. The second objective is to create a model that incorporates the production history and forecasted estimated ultimate recovery (EUR), in this case by implementing multi-segment decline curve analysis (DCA) as presented in URTeC 336 (Moridis et al.(2), 2019). To perform this analysis, we selected 38 wells from a Permian Basin dataset available to Texas A&M University. The first portion of this work involves running a sensitivity analysis to determine the spatial well relationships that may trigger movements in certain regulatory frameworks. A successful well may promote the offsetting 2P wells to PUD wells. We incorporate the methodology in SPEE Monograph 3 (2013) for estimating PUD volumes beyond immediate offset locations that can be used to estimate the Reserves and possibly Contingent Resources in some situations. The question we aim to answer is: How do we move the PUDs to proved developed producing (PDP) Reserves? In the second part of this work, we create a model which includes the production history and the forecasted EURs. As time moves forward, continuity and consistency must be maintained across the model. Assume the following scenario: we plan to move a volume "x" from 1C Contingent Resources to1P Reserves, but we can only book 0.7 x as 1P Reserves. The model must reflect the fraction of the volume x that was actually moved and how it depends on, for example, commodity price contingencies. The remaining 0.3 x volume that was not classified as Reserves must be accounted in the model. The continuity of the model through time will track the volumes, and it needs to be able to do so consistently.

Abstract The effects of the coupled reservoir and fluid properties, in addition to the geomechanical effects in the reservoir, are studied on reservoir pressure and cumulative gas produced. The behavioral variation of reservoir pressure, rock properties, and fluid properties are, numerically, studied both spatially and temporally. The coal formation, subjected to dewatering and subsequent phases, is analyzed. The study of single-phase water followed by the multiphase flow of gas and water, in the transient region, is carried out. The drained distance dynamically varies with time and expands away from the wellbore. The flow to the wellbore is either a single-phase flow or a combination of single and multiphase flow. The transition between phases modeled to maintain continuity between the flow phases. Cleat and fluid compressibility are varied with time to analyze their individual and coupled effect on flow parameters. The study details flow pattern at a time and how it changes when the time expands. The multiphase nonlinear advective term affects the flow pattern, which was, initially, diffusive in single-phase. Pressure, at any point in the reservoir, is revised when the flow regime and phases altered. The cumulative gas produced at the end of each phase and the cleat permeability, due to coupled reservoir compaction and matrix shrinkage effects, of the reservoir, in comparison with the original, is estimated. It can be observed, from the results of the study, that the flow pattern remains the same for a phase type with changing time. The flow pattern changes once the phase change from single to multiphase and vice versa. The study is novel from previous works because the cumulative production is not a function of the constant, but varying, reservoir and fluid property that helps to understand the pressure distribution in the reservoir for every phase at each time step and especially at the transitional zones. The study is beneficial to the industry in analyzing the different fluid phase flow patterns and flow associated parameters, which ultimately, after single-phase gas flow, will give us an idea of the recovery factor of methane gas and the change in reservoir petrophysical parameters from the original state.