Robust multi-objective field development optimization for the mariner asset

In this paper we present our results, challenges and learnings, over a two-year period wherein robust multi-objective optimization was applied at the Mariner asset which is being currently developed. Many different problems were solved with different objectives. These problems were formulated based on the phases of planning and development at...

Overview of the olympus field development optimization challenge

Since the early 2000's there has been a significant focus from many groups around the world towards the development and application of innovative technologies in order to improve reservoir management strategies and optimize field development plans. Benchmark studies are a very valuable way of evaluating and demonstrating the status and potential...

Drill and learn: A decision making workflow to quantify value of learning

The goal of reservoir management is to make decisions with the objective of maximizing the value creation from the oil or gas production. In order to do this models that preserve geological realism and have predictive capabilities are being developed and used. These models are commonly calibrated by using Assisted History Matching (AHM) methods...

Joint optimization of field development and water-alternating-gas recovery strategies

Alternating injection of water and gas (WAG) has been widely applied as an oil recovery strategy since the late 1950s. The expected benefits are improved macroscopic sweep, with the water and gas sweeping lower and upper zones of the reservoir respectively, and improved microscopic sweep due to various effects leading to lowering of the residual...

Well trajectory optimization constrained to structural uncertainties

A key objective in any reservoir development plan is to achieve maximum reservoir exploitation which is usually quantified using an economic objective such as net present value (NPV). A key element of such an optimized development plan is an optimized well planning scheme (number, placement and trajectories of the wells). In the well planning...

A probabilistic parametrization for geological uncertainty estimation using the ensemble Kalman filter (EnKF)

In the past years, many applications of historymatching methods in general and ensemble Kalman filter in particular have been proposed, especially in order to estimate fields that provide uncertainty in the stochastic process defined by the dynamical system of hydrocarbon recovery. Such fields can be permeability fields or porosity fields, but...

Non-parametric Bayesian networks for parameter estimation in reservoir engineering

The ultimate goal in reservoir engineering is to optimize hydrocarbon recovery from a reservoir. To achieve the goal, good knowledge of the subsurface properties is crucial. One of these properties is the permeability. Ensemble Kalman Filter (EnKF) is the most common tool used to deal with this situation. However, it is not the only way....

History matching time-lapse surface-gravity and well-pressure data with ensemble smoother for estimating gas field aquifer support - A 3D numerical study

Water influx is an important factor influencing production of gas reservoirs with an active aquifer. However, aquifer properties such as size, porosity, and permeability are typically uncertain and make predictions of field performance challenging. The observed pressure decline is inherently nonunique with respect to water influx, and large...

Gravimetric monitoring of water influx into a gas reservoir: A numerical study based on the ensemble kalman filter

Water influx into gas fields can reduce recovery factors by 10-40%. Therefore, information about the magnitude and spatial distribution of water influx is essential for efficient management of waterdrive gas reservoirs. Modern geophysical techniques such as gravimetry may provide a direct measure of mass redistribution below the surface,...

A case study of the history matching of a sector of the Norne field using the ensemble Kalman filter

In the history matching process reservoir parameters are estimated so they can be further used in a simulator to reproduce the past behaviour of the reservoir. During the last two decades the methodology evolved from manual methods to computer assisted procedures which can handle larger amounts of data. Now, when the computational power has...

A case study of the history matching of a sector of the nome field using the ensemble Kalman filter

In the history matching process reservoir parameters are estimated so they can be further used in a simulator to reproduce the past behaviour of the reservoir. During the last two decades the methodology evolved from manual methods to computer assisted procedures which can handle larger amounts of data. Now, when the computational power has...

The choice of a 'Best' assisted history matching algorithm

Computer-assisted history matching is the act of systematicalty adjusting a ‘prior’ reservoir model using measured data until its simulated production response closely reproduces the past behavior of the reservoir. Thereafler, the updated, ‘posterior’, model is expected to predict future reservoir behavior with a higher degree of confidence,...

Hydrocarbon production and reservoir management: Recent advances in closed-loop optimization technology

Petroleum production is a relatively inefficient process. For oil production, it is, generally, less than 60 % effective on a macro scale and less than 60 % effective on a micro scale. This results, commonly, in an actual oil recovery of less than 35 %. Optimization of the production process will, therefore, have a significant impact on the...

An iterative ensemble Kalman filter for reservoir engineering applications

The study has been focused on examining the usage and the applicability of ensemble Kalman filtering techniques to the history matching procedures. The ensemble Kalman filter (EnKF) is often applied nowadays to solving such a problem. Meanwhile, traditional EnKF requires assumption of the distribution's normality. Besides, it is based on the...

Multiscale ensemble filtering for reservoir engineering applications

Reservoir management requires periodic updates of the simulation models using the production data available over time. Traditionally, validation of reservoir models with production data is done using a history matching process. Uncertainties in the data, as well as in the model, lead to a nonunique history matching inverse problem. It has been...

History matching using a multiscale Ensemble Kalman Filter

Since the first version of Kalman Filter was introduced in 1960 it received a lot of attention in mathematical and engineering world. There are many successful successors like for example Ensemble Kalman Filter (Evensen 1996) which has been applied also for reservoir engineering problems. The method proposed in [Zhou et al. 2007] draws together...

Influence of ensemble geostatistics on production history matching and prediction at new wells

Automatic history matching techniques such as the Ensemble Kalman Filter (EnKF) have been shown to provide reliable results for matching and prediction at existing wells. It is much less clear if the prediction outside of existing wells improves as a result of history matching. The amount of information in production data on the spatial...

Model-reduced variational data assimilation for reservoir model updating

Variational data assimilation techniques (automatic history matching) can be used to adapt a prior permeability field in a reservoir model using production data. Classical variational data assimilation requires, however, the implementation of an adjoint model, which is an enormous programming effort. Moreover, it requires the results of one...