Sutawanir Darwis

Quantitative Analysis of the Relationship between Three Psychological Parameters Based on Swallowtail Catastrophe Model

A sudden jump in the value of the state variable in a certain dynamical system can be studied through a catastrophe model. This paper presents an application of catastrophe model to solve psychological problems. Since we will have three psychological aspects or parameters, intelligence (I), emotion (E), and adversity (A), a Swallowtail catastrophe model is considered to be an appropriate one. Our methodology consists of three steps: solving the Swallowtail potential function, finding the critical points up to and including threefold degenerates, and fitting the model into our measured data. Using a polynomial curve fitting derived from the potential function of Swallowtail catastrophe model, relations among three parameters combinations are analyzed. Results show that there are catastrophe phenomena for each relation, meaning that a small change in one psychological aspect may cause a dramatic change in another aspect.

The use of BFA simulation to evaluate reliability of composite system: Case study: Calculation of Muarakarang-Gandul 1 composite system (Islanding system which part of Jawa-Bali-Madura grid in Indonesia)

Optimization of Bacteria Foraging Algorithm (BFA) has been widely implemented in power engineering. This algorithm is emulated from escherichia coli bacterias ability in finding nutrients in the human body. This research focuses on the implementation of BFA to calculate reliability indices of power system such as Loss of Load Probability (LOLP), Loss of Load Expectation (LOLE) and Expected Energy Not Supplied (EENS). The positions of each bacterium describe the status of the generation system and the resulting fitness value is its probability. The generation system states were visited included a failure state (state which causes load curtailment or loss of load), furthermore some generation systems are configured by block of units which are identically. It is opportunity in this research to improve BFA calculation methodology by considering probability of a system state with identical combinations. New approach of reliability calculation of composite system will be proposed. When the high probability failure states are obtained, the reliability indices can be calculated accurately. To simulate the methodology, case study of BFA and Genetic Algorithms (GA) calculation of Muarakarang-Gandul 1 Composite System (islanding system which part of Jawa-Bali-Madura grid in Indonesia) will be applied.

Bayesian reservoir simulation

Reservoir reserve can be estimated from different methods, i.e. analogy, volumetric, decline curve analysis, material balance and reservoir simulation. This technique may use two methods of calculation: deterministic or stochastic. Deterministic method uses a single value for each parameter, stochastic method uses a probability model for each parameter and a simulation is used to generate the reserve distribution. Reservoir simulation applies the techniques of modeling to the analysis of the behavior of petroleum reservoir system, and refers to the hydrodynamics of flow within the reservoir. The basic of reservoir model consists of the partial differential equations which governs the flow of all fluid in the reservoir. The simulator cannot be used to predict the performance of a reservoir unless the parameter built into it describe the flow of the reservoir system. The process of modifying the existing model parameter until a reasonable match is made with the observations is called history matching, i.e., parameter estimation in reservoir models. An approach based on Bayesian methodology was proposed, where the reservoir model and parameters were updated sequentially in time, using information contained in observations from production wells. This paper addresses the issue Bayesian sequential estimation in reservoir simulation for history matching. The method consist of two steps, i.e. forecast (prior) and update (posterior). The forecast is computed using the model solution (reservoir simulation) to predict the state from time t - 1 to t. In the update step, the state forecast is updated by considering the mismatch between measurements and predictions. A single phase flow modeling is discussed . Simulation study for simple radial reservoir model shows that the Bayesian methodology can be used to history match the rese

Exploring EnKF stability under different type reservoir model

Reservoir properties estimation aims to maximize the recovery, and least squares history matching is one of strategies to achieve this target. A model is matched on historical data until it reproduced the production history. The model can be used to simulate future reservoir production. The Ensemble Kalman Filter (EnKF) is a method designed for non linear history matching. The success of EnKF affected by non linearity of reservoir model. If the reservoir model is highly non linear the EnKF might be unstable. This paper aims to explore the stability of EnKF under different type reservoir models. Three type reservoir models are considered: (1) line source (2) bounded constant rate no flow (3) three grid reservoir. The permeability is estimated based on the matching of observed pressure with predicted pressure (obtained from reservoir model). A simulation experiment is set up to show how EnKF iteration may be used to estimate the permeability. The simulation is set up for an experiment time T, time step Δt, ...

A Contribution to Stochastic Modeling Volcanic Petroleum Reservoir

The hydrocarbon reservoir data do not stem from a statistical designed experiment. Wells are drilled to pump oil and are sampled for data. In some cases the limits of the reservoir are not known beforehand from the geology. They have to be determined from the well data as information becomes available. The uncertainty about geometry of the reservoir introduces a source of error. This justifies the use of stochastic approach, which generate a range of possible reservoir models, respecting the information that is available. Stochastic simulation is an algorithm that allow generating multiple realizations of a spatial process and conditioned to reproduce the sample values at their locations in space. Stochastic simulation can handle sparse, nongridded, and correlated data. This article presents an overview of petroleum reserves estimation methods and proposes a conditional simulation approach to assess the uncertainty of the reserves evaluation on a volcanic reservoir. Conditional simulation seems to be a suitable tool for estimating the volume of hydrocarbon in place and to indicate local anomalies.