Abdollahzadeh, Asaad (Heriot-Watt University) | Reynolds, Alan (Heriot-Watt University) | Christie, Michael (Herit-Watt University) | Corne, David W. (Heriot-Watt University) | Davies, Brian J. (BP) | Williams, Glyn J.J. (BP)
Prudent decision making in subsurface assets requires reservoir uncertainty quantification. In a typical uncertainty-quantification study, reservoir models must be updated using the observed response from the reservoir by a process known as history matching. This involves solving an inverse problem, finding reservoir models that produce, under simulation, a similar response to that of the real reservoir. However, this requires multiple expensive multiphase-flow simulations. Thus, uncertainty-quantification studies employ optimization techniques to find acceptable models to be used in prediction. Different optimization algorithms and search strategies are presented in the literature, but they are generally unsatisfactory because of slow convergence to the optimal regions of the global search space, and, more importantly, failure in finding multiple acceptable reservoir models. In this context, a new approach is offered by estimation-of-distribution algorithms (EDAs). EDAs are population-based algorithms that use models to estimate the probability distribution of promising solutions and then generate new candidate solutions.
This paper explores the application of EDAs, including univariate and multivariate models. We discuss two histogram-based univariate models and one multivariate model, the Bayesian optimization algorithm (BOA), which employs Bayesian networks for modeling. By considering possible interactions between variables and exploiting explicitly stored knowledge of such interactions, EDAs can accelerate the search process while preserving search diversity. Unlike most existing approaches applied to uncertainty quantification, the Bayesian network allows the BOA to build solutions using flexible rules learned from the models obtained, rather than fixed rules, leading to better solutions and improved convergence. The BOA is naturally suited to finding good solutions in complex high-dimensional spaces, such as those typical in reservoir-uncertainty quantification.
We demonstrate the effectiveness of EDA by applying the well-known synthetic PUNQ-S3 case with multiple wells. This allows us to verify the methodology in a well-controlled case. Results show better estimation of uncertainty when compared with some other traditional population-based algorithms.
Park, J.H. (Geotechnical Engineering and Tunneling Research Division, Korea Institute of Construction Technology) | Kim, D. (Geotechnical Engineering and Tunneling Research Division, Korea Institute of Construction Technology) | Kwak, K. (Geotechnical Engineering and Tunneling Research Division, Korea Institute of Construction Technology) | Chung, M. (Geotechnical Engineering and Tunneling Research Division, Korea Institute of Construction Technology) | Chung, C.K. (Department of Civil and Environmental Engineering, Seoul National University)
One way to mitigate formation damage is to design and execute underbalanced drilling in all phases of operations such as drilling, tripping and completion. Field cases of underbalanced drilling failure showed high formation damage which motivated the need of expert systems in underbalanced drilling to achieve higher production rates.
Many underbalanced drilling operations have been analyzed, resulting in the optimum practices, as outlined in this paper. To the best of the authors' knowledge, there are no systematic guidelines for underbalanced drilling.
The objective of this paper is to propose a set of guidelines for the optimal underbalanced drilling operations, by integrating current best practices through a decision-making system based on Artificial Bayesian Intelligence. Optimum underbalanced drilling practices collected from data, models, and experts' opinions, are integrated into a Bayesian Network BN to simulate likely scenarios of its use that will honor efficient practices when dictated by varying certain parameters.
The proposed decision-making model follows a causal and an uncertainty-based approach capable of simulating realistic conditions on the use of underbalanced drilling operations. For instance, by varying the type of UBD (flow, aerated, etc), operation and formation properties the system will show optimum tripping and connection procedure. The developed model also acknowledged UBD drilling techniques in different scenarios such as fractured formations, low permeability and high permeability formations.
The model also shows optimum solutions to problems related to underbalance drilling such as well control, completion, drilling multiple reservoirs with different pressures, equipment associated with drilling.
The advantage of the artificial Bayesian intelligence method is that it can be updated easily when dealing with different opinions.