King Fahd University of Petroleum & Minerals
Computer Engineering Department
Massively Parallel Computing for Integrated
Multiwell Data in Reservoir Description
Backgroud (Dr. Abeeb A. Awotunde (PETE))
Inferring reservoir data from dynamic production data has long been done through matching the production history. However, proper integration of available production history has always been a challenge. Different production history data such as well pressure and water cut often occur at different scales making their joint inversion difficult. Furthermore, production data obtained from the same well or even the same reservoir are often correlated making a significant portion of the dataset redundant.Thirdly, the massiveness of the data recorded from wells in a large reservoir over a long period of time makes the nonlinear inversion of such data computational demanding.
The integration of multiwell production data has been proposed using the wavelet transform. The method involves the use of a two-dimensional wavelet transformation of the data space to integrate multiple production data and reduce the correlation between multiwell data representing different production responses (pressure, water cut, etc.), were treated as a single matrix of data rather than separate vectors that assume no correlation amongst datasets.
This enabled us to transform the multiwell production data into a two-dimensional wavelet domain and subsequently select the most important wavelets for history match. By minimizing the square of the Frobenius norm of the residual matrix we were able to match the calculated response to the observed response. We derived the relationship that allows us to replace a conventional minimization of the sum of squares of the l2 norms of multiobjective functions with the minimization of the square of the Frobenius norm of the integrated data. The usefulness of the approach is demonstrated using two examples. The approach proved very effective at reducing correlation between multiwell data. In addition, the method helped to reduce the cost of computing sensitivity coefficients. However, the method gave poor prediction of water cut when the datasets were not scaled before inverse modeling.
Research Plan
Graphic processing Units (GPUs) are gaining ground in high-performance computing and being considered as the leading edge of the next generation of general purpose computers. CUDA and OpenAcc are the most widely used parallel programming framework for general purpose GPU computations. This research aims at using the huge computational power of Massively Parallel Computers (MPC) for the integration of multiwell data in reservoir description. It is well recognized that the massiveness of the data recorded from wells in a large reservoir over a long period of time makes the nonlinear inversion of such data computational demanding. MPC will allow us to over scale the problem definition which improves overall results accuracy.
Project Objectives
We have set up the following research plan:
Objective-1: Investigation of parallel programming techniques in the design of a forward reservoir model (carried out as an Independent Research in PETE)
Analyze a forward reservoir model, apply finite difference to flow equations, build a grid system with a parametric time step, model the multi-phase flow, and propose a solution.
Demonstrate a sequential C program to simulate forward reservoir based on a valid reservoir model, discretization, and proper input output interfaces.
Analyze potential parallelism in a typical forward reservoir simulation system and propose techniques to orchestrate major operations in parallel to shorten execution time over a massively parallel computer
Objective-2: Integrated Multiwell Data in Reservoir Description. Feeding the available history data to the forward reservoir model to generate sample of flow solutions and build the reverse model.
Transform the multiwell production data into a two-dimensional wavelet domain,
Select the most important wavelets for history match,
Minimizing the Frobenius norm of the residual matrix to match the calculated response to the observed response, Develop a parallel programming system to efficiently implement the above two-dimensional wavelet domain, carry out selection of important wavelets, and minimize the multi-objective function.
Build a user interface to ease the process of solution develpment.
Cooperation
Our team consists of Dr. Mayez Al-Mouhamed (COE), Dr. Abeeb Awotunde (PETE), and Mr. Ayham Zaza (CCSE Ph.D. Student).