Maximizing the Benefits of Real-Time LWD Data for Production Performance Prediction
Authors: Okoroafor Rita E. , Haq Shahid , Ortenzi Luca , Zhou Wentao , Nkanga Akanimoh , Khattak Iftikhar , Okenwa Diane 
Published: May 2016

An integrated and innovative methodology for predicting production performance with real-time logging-while-drilling (LWD) data was applied in a single-well analysis. It involved building a geologic and petrophysical model, upscaling it into a numerical simulation model, and then using this dynamic model for production forecast.

The case study is a horizontal well that traversed a sequence of interbedded sands and clay beds. This well was placed in a thin oil rim identified in a pilot hole. LWD was the preferred logging method due to high well deviation. The bottomhole assembly consisted of an LWD integrated platform providing triple combo, capture spectroscopy, sigma, nuclear magnetic resonance, and formation-pressure-while drilling data.

The integration of the LWD geology, petrophysical, and reservoir interpretation in real time, combined with pressure/volume/temperature (PVT) data from an offset well, provided a good representation of the reservoir. This dynamic model was then used to predict well performance. The estimate suggested a low productivity index (PI), in line with the qualitative estimate from LWD data review. The oil-in-place results from the single-well model were compared with the results of the full field model and found to be in close agreement. Also, the results of the simulation were compared with a productivity test, and production rates were within a 20% range.

This study shows that a representative well productivity model can be derived from properly integrated LWD data. This innovative methodology demonstrates a quick way to build static and dynamic models for wells and use them for production forecasting and other engineering decisions.

Since the predictive model can be built while drilling, its estimates can aid well construction decisions (e.g., length of drain, need to sidetrack) and optimize completion strategy.

The innovative methodology can be applied in highly deviated wells, extended reach wells, and other well architectures to provide real-time productivity estimates for completion decisions.

Introduction

Knowing the production potential of a well prior to completion can help with optimizing the completion strategy. Such decisions as whether to complete the well or sidetrack it can be based on the knowledge of a well performance prediction and possibly corroborated by a well or productivity test. Various methods exist to predict production performance. Nodal analysis using offset well data can help. The production forecast from a reservoir model can be used. Note that these two methods do not make use of actual data from the well. If one has to take the step further to perform material balance and history matching for a field under production, the performance prediction of the proposed well may not be available on time to make completion decisions.

When a well is drilled, geological, petrophysical, and reservoir data available through logging give a qualitative idea of the producibility of a well. For example, a large net pay with high oil saturation and low bound-fluid volumes can indicate that the well will produce well. Since well productivity involves dynamics (fluid flow), measurement of rock and fluid properties such as permeability, viscosity, and a pressure gradient, at a minimum, will give a quantitative estimate of the well's productivity.