Selection of Infill Drilling Locations Using Customized Type Curves

Abstract

This paper discusses a new workflow to stochastically estimate the performance of infill locations in a mature oil or gas field. Usually performance evaluations for infill wells are conducted using either much generalized statistical methods or numerical simulation. Both approaches have a significant drawback; the prior being quick however very often lacking in accuracy, the latter being very accurate however usually very complex in setup and computation. The presented workflow is a new approach to infill well performance prediction that combines speed and reasonable accuracy. The workflow generates a set of key performance indicators of existing wells derived from historic dynamic data (fluid production rates, pressures, etc.), static data (reservoir properties, etc.) and predicted data (simplified production forecasts). The wells are then grouped according to the similarity of their KPIs. The production profiles of the wells within the same group are combined to a type curve that is described by the most likely production profile and an associated uncertainty range. A data-driven expert system is used to identify and capture the correlations of the parameters such as geographic locations, well spacing, reservoir properties and the group membership (equivalent to type curve). This expert system can then be applied to any location in the field in order to determine the most likely group membership of a potential infill well. The classification of an infill well to a group is hereby not necessarily unique; the expert system might classify an infill well into several groups and assign a probability of occurrence for each of the groups. A Monte Carlo routine is then applied to forecast the performance of the infill locations honoring the respective probability of occurrence of each type curve. The presented approach has been successfully applied for infill well selection in a statistical field development study for YPF in the Argentinean San Jorge Basin. Introduction Despite the long production history with more than 20 000 wells, the current methodology to make decisions still takes a long and costly path consisting of testing, plugging and stimulation procedures. The authors believe that, in parallel to the reservoir modeling studies, YPF can make use of the immense array of well data and the wisdom acquired over one century of production history, to produce a set of innovative practices to boost the efficiency of their current operations. Therefore a statistical approach based on the power of emerging computing tools for data mining can assist YPF to recognize patterns and develop methodologies which are strong enough to tackle the aforementioned technical challenges, transforming these opportunities in real bottom line results. The goal of the study is to generate sound technical arguments to formulate an innovative strategy to accelerate the exploitation of oil and gas assets of YPF in the San Jorge Basin, Argentina. The workflows of interest areidentify candidates for infill drilling locations,propose a field development strategy based on lessons learned from the past in order to know the size of the business from an economic point of viewidentify benefits through optimizing infill locations using data mining methodology. The objective of this paper is to present the methodology and results obtained during the analysis phase as value promise for field development.

Using Bayesian Network to Develop Drilling Expert Systems

“…The numerical simulation is accurate but requires complex steps and computations. The objective of this paper was to select the optimum infill locations using an integrated data mining charts by looking into past production performance and trying to predict future performance of current wells, Al-Kinani et al (2009).…”

“…A score between 0 and 100 can describe the outcome. A value of 100 means the best producer well and 0 means the worse, Al-Kinani et al (2009).…”

Using Marketing Technology in Reservoir Engineering: The Application of Data Driven Predictive Analysis on a Mature Oil Field

“…The challenge addressed is to find optimal well locations for the next drilling campaign and generate reliable oil production forecasts in a short timeframe. The nomenclature in this paper is aligned with (Stone, 2008), the workflow steps are taken over from (Mohaghegh, 2003) and the practical execution method is similar to (Al Kinani, 2009). A new element is the strong integration with a full field static model.…”

“… Preferably, an up to date static model supported by petrophysical data is available to make predictions far away from existing wells, in previously undrilled areas. However, if a static model is absent, still predictions can be made on the basis of other parameters (Al Kinani, 2009). …”