Intelligent time-successive production modeling

2015

Production data analysis is an important tool for estimating important reservoir parameters. In particular, determining the average reservoir pressure (pav) and tracking its change with time is critical to analyzing and optimizing reservoir performance. The traditional method for determining pav involves pressure buildup tests. A direct method for estimating (pav) from flowing pressures and rate data is available. However, the method is for an idealized case that assumes constant production rate during pseudo steady-state (PSS) flow, which is not generally true for real wells. This research extends that approach so that it can be used to analyze field data with variable rates/variable pressures during boundary-dominated flow (BDF). For gas reservoirs, pseudopressure and pseudotime functions are used to linearize the gas flow equation and enable the liquid diffusivity solution to satisfy gas behavior when analyzing gas test data. This project investigated when the use of pseudo time becomes necessity, and developed a technique to complete the linearization of diffusivity equation without using conventional pseudo time. A further objective of this research included extending our modified approach into a multi-well system. This modified approach is based on a combination of rate-normalized pressure and superposition-time function. The mathematical basis is presented in support of this approach, and the method is validated with synthetic examples and verified with field data. This modified approach is used to estimate average-reservoir pressure, calculate both connected oil volume and reservoir drainage area as a function of time, and provide a reasonable estimation of the reservoir's shape factor. These calculations, allowing the reservoir performance and management to be properly evaluated. v ACKNOWLEDGMENTS First and foremost, all the praise and gratitude be to Allah, the sole Creator and Sustainer of the Universe, he is the one that deserves all the praise. Peace be upon his prophet Muhammad. I would like to express my most sincere appreciation to my advisor, Dr. Flori for his invaluable guidance, encouragement, and his patience in helping bring this work to completion. Appreciation is also extended to Mr. Shah Kabir, global reservoir engineer at Hess Corporation, for his invaluable help and suggestions that played a vital role to get part from this work accepted for publishing.

Numerical simulations and decline curve analysis are classic tools used for predicting reservoir performance. Numerical simulations are a very complex tool, offering a nonunique solution with a high degree of uncertainty. Decline curve analysis does not take into account opening or closing intervals and variable injection rates. In this study, the authors designed a feedforward backpropagation neural network model as an alternative technique for predicting oil reservoir production performance. Real historical production data obtained from a Libyan oil field was used to train the network. This training network can serve as a practical reservoir production management tool.

International Petroleum Technology Conference, 2007

For a matured oil field like Bahrain Field with a long production history, it is required to identify underperforming areas, infill wells and upgrade the reserves. This paper describes the application of a practical process (1) to develop systematic workflow for production optimization and reservoir analysis; (2) Identify and highlight reservoir trends, patterns and anomalies; (3) Identify and highlight the under performing wells/areas and recommend solutions, and (4) Identify essential patterns for consideration in overall development plan. It is required to quickly adopt assessment methods for such a mature field. The area used for the study consists of 431 wells in Mauddud reservoir which is one of the major producing zones. The challenge was to evaluate large data sets in a short time and cost-effective manner.

2009

SUMMARY Traditional reservoir simulation and modeling is a bottom-up approach. It starts with building a geological model of the reservoir followed by adding engineering fluid flow principles to arrive at a dynamic reservoir model. The dynamic reservoir model is calibrated using the production history of multiple wells and the history matched model is used to strategize field development in order to improve recovery. Top-Down Intelligent Reservoir Modeling approaches the reservoir simulation and modeling from an opposite angle by attempting to build a realization of the reservoir starting with well production behavior (history). The production history is augmented by core, log, well test and seismic data in order to increase the accuracy and fine tune the Top-Down model. The model is then calibrated (history matched) using the most recent wells as blind dataset. Although not intended as a substitute for the traditional reservoir simulation of large, complex fields, this novel approa...

Advances in Neural Networks – ISNN 2019, 2019

We develop a data-driven model, introducing recent advances in machine learning to reservoir simulation. We use a conventional reservoir modeling tool to generate training set and a special ensemble of artificial neural networks (ANNs) to build a predictive model. The ANN-based model allows to reproduce the time dependence of fluids and pressure distribution within the computational cells of the reservoir model. We compare the performance of the ANN-based model with conventional reservoir modeling and illustrate that ANN-based model (1) is able to capture all the output parameters of the conventional model with very high accuracy and (2) demonstrate much higher computational performance. We finally elaborate on further options for research and developments within the area of reservoir modeling.

Engineering and Technology Journal, 2015

Reservoir operation forecasting plays an important role in managing water resources systems. Artificial Neural Network (ANN) model was applied for Mosul-Dam reservoir which is located on Tigris River, which the objectives of water resources development and flood control. Feed-forward multi-layer perceptions (MLPs) are used and trained with the back-propagation algorithm, as they have a high capability of data mapping. The data set has a period of 23 years from 1990 to 2012..The Input data were inflow (I t), evaporation (E t), rainfall (R t), reservoir storage (S t) and outflow (O t). The best convergence after more than 1000 trials was achieved for the combination of inflow (I t), inflow (I t-1), inflow (I t-2), evaporation (E t), reservoir storage (S t), rainfall (R t), outflow (O t-1) and outflow (O t-2) with error tolerance, learning rate, momentum rate, number of cycles and number of hidden layers as 0.001, 1, 0.9,50000 and 9 respectively. The coefficient of determination (R 2) and MAPE were (0.972) and (17.15) respectively. The results of ANN models for the training, testing and validation were compared with the observed data. The predicted values from the neural networks matched the measured values very well. The application of ANN technique and the predicted equation by using the connection weights and the threshold levels, assist the reservoir operation decision and future updating, also it is an important Model for finding the missing data. The ANN technique can accurately predict the monthly Outflow.

All Days, 2007

Intelligent Production Data Analysis – IPDA, is a new methodology for Reservoir Characterization based only on monthly production rate data. This technique combines conventional methods of production data analysis (decline curve analysis, type curve matching and history matching) with intelligent systems. The study targets the validation of this methodology under a controlled environment, attempting three main objectives: Identifying Sweet Spots, Forecasting Reserves and recognizing under-performer wells. The study investigates the behavior of five different reservoirs, modeled using a commercial simulator. The structure, parameters and heterogeneity of each configuration was inspired by existing formations. Records of production rate data were generated from the simulated fields (both single and multi-layer formations) and used as input to perform an "Intelligent Production Data Analysis". The findings highlight strength of this technique in tracking the fluid movement in...

International Journal of Software & Hardware Research in Engineering, 2021

The Material Balance (MBAL) software is a component of the Petroleum Experts' Integrated production modeling (IPM) tool kit and it is used for the development of the Reservoir model. It allows and improves the understanding of the field behavior in terms of the field performance, production, flow behavior and optimization of the field at any given time of the lifespan of the field. The paper demonstrates reservoir performance prediction using dynamic MBAL and predictive material balance method after proper history matching of the models and validated with appropriate data. The objective of the research is to set up a basic Reservoir Model with the PVT analysis, production and tank data. The paper further shows history matching by matching Original Hydrocarbon in place (OHIP) and Aquifer size and plots with required parameters against simulated results and prediction of future hydrocarbon production.

All Days, 2014

Performing look back studies to evaluate the economic and technical impacts of filed management decisions, is not a common occurrence in our industry. Even when such studies are performed, the results are hardly ever published for evaluation and scrutiny by the larger community of industry professionals. This paper presents such a study in the case of a mature giant oil field in the Middle East. This prolific mature asset that includes more than 160 production wells has been the subject of peripheral water injection for many years to maintain pressure and help displace oil toward the production wells. Production was restricted to 1,500 bbls of fluid per day per well to avoid excessive water production as well as pulling oil from an over produced overlaying prolific reservoir. In 2005 a reservoir management study was commissioned to evaluate the impact of rate relaxation in this asset. The objective was to explore the likelihood of increasing oil production from the asset while minim...