In this work an analytical solution for the inverse problem of a heterogeneous reservoir, composed by several infinite acting homogenous layers, is presented. The solution comprises a small set of ordinary algebraic equations, providing an explicit solution for the inverse problem. The layers may differ in thickness, porosity, fluid viscosity, total compressibility and initial pressure, which are input data. The proposed Method calculates the radial permeability and the wellbore skin factor for each individual layer and requires Production Logging data. The Method's outcomes are important for both mature fields and new discoveries, providing valuable information for enhanced oil recovery techniques and accurate data for geological modeling. In addition, the method is a useful tool for assessing acid job diversion performance, especially on carbonate reservoirs, where acidizing procedures are largely employed. The proposed Delta Transient Method focuses on providing simple calculations and delivering robust results. Operational feasibility of data acquisition has also been taken into account for the development of the Method. The main features of the present work are: The Method calculates the permeability and skin for each individual layer, for an unlimited number of layers; The Method has been designed to be applied to a relatively short single rate flow period (only few hours of drawdown/injection); There is no need for rate changes. Nonetheless, the Method works with rate changes, as long as the principle of superposition is considered; Only two PLT (production-logging tool) flow profiles at distinct times are required for full application of the Method. No stationary data are needed; The calculations for each layer are performed with a small set of algebraic equations, which can be easily modeled in any ordinary spreadsheet software. No graph analyses are required. The Method has a strong operational-practical drive, which consists of demanding only few hours of rig time for data acquisition. Moreover, once permeability and skin for each layer are obtained, a reasonableness check is provided as a final step to the Method. The purpose of this check is to guarantee reliability on the resulting outcomes. Issues concerning formation crossflow, wellbore storage and high transmissibility systems are also addressed. A successful case study is presented, where it took only 10 hours to acquire all necessary data.
The method proposed in this work calculates the permeability and wellbore skin factor for each individual layer in a multilayered reservoir, fully addressing the superposition principle for the multi-rate test scenario. The method provides an explicit solution for the inverse problem, where the main inputs are downhole pressure data, production logging data and the surface rate schedule, as well as other commonly known parameters. The Method has a strong operational drive, which consists of demanding only few hours of rig time to acquire the necessary data. The main contribution of this work is to allow the application of the method to multi-rate schedules, providing more flexibility for the data acquisition scheme. Regarding production logging data, only two flow profiles at distinct times are required for full application of the Method. No stationary data are needed. The calculations for each layer are performed with a set of algebraic equations, which can be easily modeled in any ordinary spreadsheet software. No graph analyses are required. The resulting skin distribution has a great impact on acid job designing techniques, enabling the assessment of diversion effectiveness across the wellbore, especially on carbonate reservoirs, where acidizing procedures are largely employed. The individual layer permeabilities provide more accurate data for geological modeling, potentially resulting in more reliable production curves and reserves estimates. As a final step to the Method, once permeability and skin for each layer are obtained, a reasonableness check is provided. The purpose of this check is to guarantee reliability on the resulting outcomes. A successful case study is presented, where it took 9 hours to acquire all necessary data.
Summary The objective of this work is to develop and train feedforward artificial neural networks (ANNs) on the forecasting of layer permeability in heterogeneous reservoirs. The results are validated by comparing the model outputs with permeability curves computed from production logging data. Production logs are used as targets to train the model. A flow-profile interpretation method is used to compute continuous permeability curves free of wellbore skin effects. In addition, segmentation techniques are applied to high-resolution ultrasonic image logs. These logs provide not only the image of the mega- and giga-pore system but can also identify the permeable facies along the reservoir. The image segmentation jointly with other borehole logs provides the necessary features for the network training process. The proposed neural network focuses on delivering reliable and validated permeability curves. Its development accounts for formation skin factor, as well as nongeological noise usually found in ultrasonic image logs. The procedure is tested on both synthetic and field data sets. The estimations presented herein demonstrate the model's ability to learn nonlinear relationships between geological input variables and reservoir dynamic data even if the actual physical relationship is complex and not known a priori. Although the preprocessing stages of the procedure involve some expertise in data interpretation, the neural-network structure can be easily coded in any programming language, requiring no assumptions on physics in advance. For the case studies presented in this work, the proposed procedure provides more accurate permeability curves than the ones obtained from conventional methods, which usually fail to predict the permeability measured on drill-stem tests conducted in dual-porosity reservoirs. The novelty of this work is to incorporate dynamic production-logging (PL) data into the permeability-estimation workflow. Correction Notice: The preprint paper was updated from its originally published version to correct Fig. 17 on page 11. An erratum detailing the change is included in the Supporting Information section below.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.