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Summary It is common to produce some percentage of water during the oil‐extraction process. Conventionally, some water‐disposal wells are drilled in an oil field to inject these useless and hazardous waters. Mineral scale formation is a critical issue in water‐injection wells and may result in well plugging and an injection rate decrease in these wells. The two steps of mineral scale formation are scale precipitation and scale deposition. Two main mechanisms of inorganic scale precipitation are incompatibility between injected water and reservoir formation water and changes in the thermodynamic state of injected water. The injectivity of the well decreases because of deposition of supersaturated precipitated scales through the well column and near‐wellbore region. Currently, limited research has been done to evaluate inorganic scale deposition, and most of the research is limited to calculation of total scaling by commercial software. In this study, the mineral scale precipitation is evaluated by software modeling and laboratory experiments in an Iranian oil field, and the effect of the scale deposition phenomenon is assessed on permeability impairment and injection rate decrease. One of the major novelties of this work is simulation of various scale‐deposition models by coupling MATLAB® software coding and a reservoir simulator. The accuracy of different deposition models is analyzed by comparing them with field data (real water‐injection well) and laboratory tests (coreflooding test). Finally, our simulation results show that a single deposition model could not exactly predict the scaling phenomena in the studied carbonate reservoir that is supersaturated with CaCO3 and CaSO4. It is recommended to improve the scale‐formation prediction with a mixed deposition model supported by reliable static/dynamic modeling and experimental analysis.
Abstract Characterization of hydraulic fracture system in multi-fractured horizontal wells (MFHW) is one of the key steps in well spacing optimization of tight and shale reservoirs. Different methods have been proposed in the industry including core-through, micro-seismic, off-set pressure data monitoring during hydraulic fracturing, pressure depletion mapping, rate-transient analysis, pressure-transient analysis, and pressure interference test. Pressure interference test for a production and monitoring well pair includes flowing the production well at a stable rate while keeping the monitoring well shut-in and recording its pressure. In this study, the coupled flow of gas in hydraulic fractures and matrix systems during pressure interference test is modeled using an analytical method. The model is based on Laplace transform combined with pseudo-pressure and pseudo-time. The model is validated against numerical simulation to make sure the inter-well communication test is reasonably represented. Two key parameters were introduced and calculated with time using the analytical model including pressure drawdown ratio and pressure decline ratio. The model is applied to two field cases from Montney formation. In this case, two wells in the gas condensate region of Montney were selected for a pressure interference test. The monitoring well was equipped with downhole gauges. As the producing well was opened for production, the bottom-hole pressure of the monitoring well started declining at much lower rate than the production well. The pressure decline rate in the monitoring well eventually approached that of the producing well after days of production. This whole process was modeled using the analytical model of this study by adjusting the conductivity of the communicating fractures between the well pairs. This study provides a practical analytical tool for quantitative analysis of the interference test in MFHWs. This model can be integrated with other tools for improved characterization of hydraulic fracture systems in tight and shale reservoirs.
Abstract A seven-step workflow to help subsurface teams establish an initial thesis for optimal completion design (cluster spacing, proppant per cluster) and well spacing in emerging / under-explored resource plays is proposed and executed for the Powder River Basin Niobrara unconventional oil play. The workflow uses Rate Transient Analysis (RTA) to determine the parameter and then walks the reader through how to sequentially decouple the parameter into its constituent parts (frac height (h), number of symmetrical fractures achieved (nf), permeability (k) and fracture half-length (xf)). Once these terms were quantified for each of the case study wells, they were used in a black oil reservoir simulator to compare predicted verses actual cumulative oil performance at 30, 60, 90,120 & 180 days. A long-term production match was achieved using xf as the lone history match parameter. xf verses proppant per effective half-cluster yielded an R value of > 0.90. 28 simulation scenarios were executed to represent a range of cluster spacing, proppant per cluster and well spacing scenarios. Economics (ROR and/or NPV10/Net Acre) were determined for each of these scenarios under three different commodity pricing assumptions ($40/$2.50, $50/$2.50 and $60/$2.50). An initial thesis for optimal cluster spacing, proppant per designed cluster and well spacing were determined to be 12’, 47,500 lbs and 8-14 wells per section (based on whether or not fracture asymmetry is considered) when WTI and Henry Hub are assumed to be $50 & $2.50 flat.
A measurement of the ability of a fluid to flow through a rock. The quality of the soil that enables water to move through the profile. Permeability is measured as the number of inches per hour that water moves through the saturated soil. A measurement of the ability of a fluid to flow through a rock. The quality of the soil that enables water to move through the profile.
Summary Reservoir depletion is known to reduce the porosity and permeability of stress-sensitive reservoir rocks. The effect may substantially hinder the productivity index (PI) of producing wells. This study presents analytical solutions for the time-dependent and steady-state well PIs, respectively, of a bounded, disk-shaped, elastic reservoir with no-flow and constant-pressure conditions at the outer boundary. A combination of Green's functions, the Laplace transform method, and the perturbation technique is used to solve the governing nonlinear partial differential equations of the considered coupled problems of flow and geomechanics. Dimensional analyses based on the Buckingham theorem are conducted to identify the dimensionless parameters groups of each problem and to express the resulting analytical solutions in the dimensionless form. In addition, necessary corrections to an existing error in the reported Green's functions for the induced strain field of a ring-shaped pressure source within an elastic half-space (Segall 1992) are made. The corrected Green's functions are used to obtain the strain induced by the pore fluid pressure distribution within a depleting disked-shaped reservoir. Consequently, a corrected permeability variation model compared to our previously published, time-independent solution for rate-dependent PI (Zhang and Mehrabian 2021a) is presented. Finally, a mechanistically rigorous formulation of the permeability modulus parameter that commonly appears in the pertinent literature is suggested. In addition to the in-house developed finite-difference solutions, the presented analytical solutions are verified against results from the finite-element simulation of the same problems using COMSOL® Multiphysics (2018). The obtained rate-dependent PI of the reservoir is controlled by four dimensionless parameters, namely, the dimensionless rock bulk modulus, the Biot-Willis effective stress coefficient, Poisson's ratio, and rock initial porosity. The pore fluid pressure solution is shown to asymptotically approach the corresponding flow-only solution for large values of the dimensionless rock bulk modulus. Parametric analysis of the solution suggests that the well productivity loss has a reverse relationship with the dimensionless bulk modulus and initial porosity of the rock, whereas a direct relationship is identified with Biot-Willis effective stress coefficient and Poisson's ratio. Compared to the reservoir with a constant-pressure outer boundary, the PI of a reservoir with a no-flow condition at the outer boundary is shown to be more significantly hindered by the stress sensitivity of the reservoir rock.
Johnson, Caroline (Heriot-Watt University–Edinburgh (*Corresponding author) | Sefat, Morteza Haghighat (email: email@example.com)) | Elsheikh, Ahmed H. (Heriot-Watt University–Edinburgh) | Davies, David (Heriot-Watt University–Edinburgh)
Summary In the next decades, tens of thousands of well plugging and abandonment (P&A) operations are expected to be executed worldwide. Decommissioning activities in the North Sea alone are forecasted to require 2,624 wells to be plugged and abandoned during the 10-year period starting from 2019 (Oil&Gas_UK 2019). This increase in decommissioning activity level and the associated high costs of permanent P&A operations require new, fit-for-purpose, P&A design tools and operational technologies to ensure safe and cost-effective decommissioning of hydrocarbon production wells. This paper introduces a novel modeling framework to support risk-based evaluation of well P&A designs using a fluid-flow simulation methodology combined with probabilistic estimation techniques. The developed well-centric modeling framework covers the full range of North Sea P&A well designs and allows for quantification of the long-term (thousands of years) evolution of hydrocarbon movement in the plugged and abandoned well. The framework is complemented by an in-house visualization tool for identification of the dominant hydrocarbon flow-paths. Monte Carlo methods are used to account for uncertainties in the modeling inputs, allowing for robust comparison of various P&A design options, which can be ranked on the basis of hydrocarbon leakage risks. The proposed framework is able to model transient conditions within the well P&A system, allowing for the development of new key performance indicators (e.g., time until hydrocarbons reach surface and changes in hydrocarbon saturation within the P&A well). Such key performance indicators are not commonly used, because most published work in this area relies on steady-state P&A models. The developed framework was used in the assessment of several P&A design cases. The results obtained, which are presented in this paper, demonstrate its value for supporting risk-baseddecision-making by allowing for quantitative comparison of the expected performance of multiple P&A design options for given well/reservoir conditions. The framework can be used for identifying cost-effective, fit-for-purpose P&A designs, for example by optimizing the number, location, and length of wellbore barriers and evaluating the effectiveness of annular cement sheath remedial operations. Additionally, this framework can be used as a sensitivity analysis tool to identify the critical parameters that have the greatest impact on the modeled leakage risks, to guide data acquisition plans and model refinement steps aimed at reducing the uncertainties in key model parameters.
Summary The nonparametric transformation is a data-driven technique, which can be used to estimate optimal correlations between a dependent variable (response) and a set of independent parameters (predictors). This study introduces a systematic methodology using the nonparametric transformation concept and the alternating conditional expectation (ACE) algorithm to estimate the effective gas permeability using conventional logs and the core data. The ACE algorithm was employed in the current work using the MATLAB® (The MathWorks, Inc., Natick, Massachusetts, USA) code and the open-source GRaphical ACE (GRACE) software (Xue et al. 1997) for deriving the optimal nonparametric correlations for predicting the permeability. The methodology was applied to a heterogeneous formation [Bahariya (BAH)] in Egypt to understand its characteristics and predict its permeability more accurately. The BAH Formation is considered one of the main sources for oil production throughout the Western Desert (WD) of Egypt. The cumulative oil production from the BAH Formation is estimated to be approximately 40% of the total WD production. The reservoir characteristics of the BAH Formation range from highly permeable to tight sandstone interbedded with shale and siltstone. It usually depicts low-resistivity and low-contrast (LRLC) log behavior. Thus, regional and accurate determination of the reservoir permeability for the different rock units of the BAH Formation across the WD is a challenge. Conventional well log data from approximately 100 cored wells and corresponding 5,500 core measurements were used to provide a regional permeability correlation that can be used in a large number of reservoirs. The methodology of this work included two main steps: Applying the nonparametric transformation technique to identify the collective log responses for deriving optimal correlation Predicting the permeability profiles using the selected log responses The model was applied to many wells that address different petrophysical characteristics of the BAH Formation. The established permeability profiles showed reliable correlation coefficients relative to the measured core data. The correlation coefficient was 0.893 for the training data points (75% of the collected database) and 0.913 for the testing data points (25% of the collected database). In addition, the mean absolute percentage error (MAPE) between the predicted and the measured permeability for the training and testing data points were 5.93 and 4.14%, respectively. Permeability prediction using ACE is compared with other techniques such as k-ϕ crossplots, multiple linear regression (MLR), Coates, and Wyllie-Rosecorrelations. This work is considered an original contribution to present regional permeability prediction correlations using the conventional well logs for reservoir characterization and simulation applications. The ACE algorithm was successfully applied to the BAH Formation and proved its capability to identify the best predictors that are required to establish a rigorous model.