Combining a Surveillance Testing Workflow into the Assisted History Matching Process to Reduce Uncertainties in a SAGD Reservoir

Abstract

Assisted History Matching (AHM) is a technology that enables reservoir engineers to: automatically create multiple realizations by combining different choices of the reservoir parameters (uncertainties); run the simulation jobs (experiments); analyze the results to determine an objective function such as history match quality for each realization; and then set up new simulation jobs by using an optimizer to determine the parameters combinations. The history-matched models can then be used in optimization on a production process for the purpose of optimizing several depletion development plans through the Closed-Loop Reservoir Management (CLRM) workflow. The system is able to update the models as field measurements become available, and the reservoir management can be changed from periodic to a near-continuous process. The CLRM has become popular in fields where modern sensors can bring huge quantity of real-time information. In this paper, a workflow has been developed to test if the existing field surveillance or/and new surveillance add value to the AHM process. To do this, a single deterministic reservoir description was designated as the truth case, and using an exploitative optimizer a range of equivalent history matched models were generated and tested for fidelity to the truth case. Two different formulations of the objective function were created, the primary containing only rate measurements in well pairs and a second that further included temperature measurements in observation wells to see how these additional observations would alter the quality of the prediction. An additional set of observations for future temperature observations in the six months after the end of the history match were created, again to test how such observations reduced the uncertainty range of the reservoir in future outcomes. We are also testing if there is a difference between observation well locations, and where an ideal observation well could be located to find the clearest signal indicating future performance. The reservoir model is that of a 3D Steam Assisted Gravity Drainage (SAGD) thermal reservoir. Two horizontal well pairs provide steam and allow production, and ten vertical observation wells are distributed throughout the SAGD reservoir with five stations along each well for pressure and temperature measurements. The results showed that the temperature measurement in five observation wells failed to reduce the uncertainty range in the cumulative field oil production and also failed to exclude models that have the dangerous characteristic of being a good history match yet a poor prediction. The uncertainty in the future reservoir outcomes can be reduced by 72 % when the temperature measurements in ten observation wells were used in the AHM process for a period of six months following the first year and a half production, potentially indicating when a key signature becomes observable. These observations were completed in different areas throughout the reservoir and had captured the development of the steam chamber with...

Assimilation of Time-Lapse Temperature Observations and 4D-Seismic Data With the EnKF in SAGD Petroleum Reservoirs

“…The Bayes' rule can be expressed, as shown in Eq. 5, which states that posterior probability distribution pðz t jd t Þ of the system at Timestep t is proportional to a product of some prior probability distribution pðz t Þ and data likelihood pðd t jz t Þ at the same Timestep t (Tarantola 2005;Evensen 2009). These probability-density functions are presented mathematically in Eqs.…”

“…A solution to the inverse problem is not unique, which means there is more than one solution that satisfies the problem settings and honours the data. It is not obvious which solution is the closest to the reality and, therefore, multiple realizations are analyzed to assess uncertainty in the prediction (Tarantola 2005).…”