SPE Western Regional Meeting,
27-29 May 2010,
Anaheim, California, USA
Production data analysis has extensively been applied to predicting the future
production performance and field recovery. These applications are mostly on a
single well basis. This paper presents a new approach to production data
analysis using Artificial Intelligence (AI) techniques where production history
is used to build a field-wide performance prediction models. In this work AI
techniques and data driven modeling are utilized to predict future production
of both synthetic (for validation purposes) and real field cases.
In the approach presented in this article production history is paired with
geological information from the field to build datasets containing the
spatio-temporal dependencies amongst different wells. These dependencies are
addressed by compiling information from Closest Offset Wells (COWs) that
includes their geological and reservoir characteristics (spatial data) as well
as their production history (temporal data).
Once the dataset is assembled, a series of neural networks are trained using
backpropagation algorithm. These networks are then fused together to form the
“Intelligent Time-Successive Production Modeling“(ITSPM) system. This technique
only uses the widely available measured data such as well logs and production
history of existing wells to predict a) future performance of the existing
wells, b) production performance of the new (infill) wells and c) the initial
hydrocarbon in place using a “volumetric-geostatistical” method.
To demonstrate the applicability of this method, a synthetic oil reservoir is
modeled using a commercial simulator. Production and well-log data are
extracted into an all-inclusive dataset. Several neural networks are trained
and validated to predict different stages of the production. ITSPM method is
utilized to estimate the production profile for nine new wells in the
reservoir. Furthermore, ITSPM is also applied to a giant oil field in the
Middle East with more than 200 wells and forty years of production history.
ITSPM’s production predictions of the four newest wells in this reservoir are
compared their actual production.
Two of the most influential pieces of information in decision making and field
developments are our depth of knowledge about the reservoir’s state of
depletion and the estimation of the remaining reserves. These become more
important in brown fields which most of wells are in their decline period and
new wells can easily become non profitable if not drilled in the right
There are several techniques enabling the reservoir engineers to have a
reservoir model that is capable of predicting future behavior of the reservoir
under different development strategies. These models are normally based on
numerical solutions of the fluid flow equation and require fairly accurate data
and measurements about the formation. Furthermore, acquiring such data and
measurements are expensive and building full field models to use these data in
a cohesive manner can be financially prohibitive considering the computational
and human resources required.
In contrast, instead of lengthy and expensive numerical solutions, analytical
solutions are simpler and cheaper. These solutions are normally limited to
single well based analyses with many limiting homogeneity assumptions. Although
these solutions are much easier to develop and they do not need vast amount of
data nor computer power, their deliverability is limited.
Relying on availability of large amount of data and measurements about the
field is not always a realistic solution. Therefore the numerical solutions are
not always practical. Moreover, single well analysis techniques are not always
good choices for field development strategy and decision makings.
Brown fields with marginal production rates or old fields without
state-of-the-technology studies, are not the best candidates for costly
numerical simulation models. In some cases single well numerical models are
built for some fields; these models limit the analysis to one well and don’t
give a full field understanding of the reservoir.