Category: Application Notes

 

The most distinguished feature of ALPOS is failure prediction, it will allow us to see what’s most likely to happen in near future with monitored wells, so either we can try to optimize or get ourselves ready for a replacement.

 

Why we need prediction?

Short answer is to minimize down time related to replacing the artificial lifting system. The common practice is when one well fails, ESP operator gets a call out, and then troubleshooting, redesign, wait for the design to be approved, prepare for replacement, wait for hoist/rig. Minimum down-time of one well starts at 7 days, most of the down time is on “waiting”. We can try to eliminate all other wait times with other features of ALPOS, but there is one thing out of our control – hoist/rig schedule. If we can get our failed well on next week’s schedule we consider ourselves lucky enough. If two or more wells fail in the similar period of time and they belong to the same operator, the wait time would be a nightmare. But it doesn’t have to be that way. With the failure prediction algorithm, ALPOS will identify the probability of the failure in 3 months, 6 months and 1 year. If the figure reaches 80% for the well expected to fail in 3 months, considering other parameters ALPOS would likely to give out the suggestion to elective pull and put it on the nearest/non-colliding schedule to help the company better planning with their asset.

In the above example, the system suffers a lot from the fluctuation and when it comes closer to the end of its run-life, the well unplanned trips for multiple times. Base on the algorithm of ALPOS, by Mar-2 ALPOS will identify the chance of failure in 3 months for this well to be as high as 95%. So, if this alert can be handled properly, the potential one month (multi-million dollars worthy) down-time due to multiple colliding/short of stock could be prevented.

 

 

Design compatibility has a huge impact on both run-life and production, it is believed to be equally important to the well condition impact and equipment overall quality (manufacturing, installation and daily management). So, it’s unarguably important.

Even though we have very advanced tools and knowledge to make compatible design these days, it is still very common that when one system is installed it could not perform under its best range. The reasons contribute to this fact includes, inadequate latest well data, unavailability of designed items as well as human error. ALPOS is designed to eliminate all of those.

a) The number one task for ALPOS is to collect real-time data to feed back to the server and analyzing the performance to determine how things can be done differently in the next life-cycle where ALPOS could contribute to the design, so the design with ALPOS involved is well informed and data equipped.

b) With ALPOS monitoring one well, it would suggest the best design for the next cycle and update the design in case any new activity matters enough to trigger a change. In the meantime, working in conjunction with its life-prediction module, ALPOS will send out alert information to the client to inform the ESP operator to prepare replacement system according to the very specific design at least 3 month before the failure. This would avoid the circumstances when one well fails, and the suggested design is not available, so the “next best thing” available is installed. Very often, that “next best thing” will cost the company a high percentage of production loss and a confirmed shorter run-life.

c) Human error is preventable, and what ALPOS doing is that it will put out a suggestive design, and the Application Engineer working with ALPOS will overlook the design and choose to approve it or put up another design to override it. To choose to override, the AE is believed to have adequate information and strong confidence with which that version of design will outperform the ALPOS one, which means an error is very unlikely. Moreover, when override happens the performance in the next cycle will be monitored by ALPOS, if the result outperforms the ALPOS prediction then that human design will be injected into ALPOS as a calibration for the Design Algorithm of ALPOS.

With the help of ALPOS, what will be improved includes:

Performance

By comparing before and after the ALPOS redesign, we will find a significant improvement with the baseline of the production. 10%~15% on the production/target rate.

 

Consistency

With the help of “predict-alert” feature of ALPOS, it’s expected that for most of the failure, a suitable designed set of equipment will be in place for replacement.
So, what can be found in the current situation, like in the fourth life cycle when a non-compatible equipment was installed, the baseline of the production will move away from the target again.

With ALPOS in place, that situation will be strictly monitored and prevented.

 

Run-life

With compatible design, the run-life will be extended to a reasonable level and it would be a stable one when monitored under longer time span.
With ALPOS, the ARL (Average Run Life) will be 35% longer with joint effort of the design, fluctuation countermeasure and prevention of unplanned shut-down.

 

There are multiple reasons for unplanned shutdown: under load, over load, over current, over heat, etc. For each particular reason, there could be a way to proactively response. However, what happens very frequently now is that in order to protect the system from get itself burnt, it is very often chose to trip the well rather than solutions with better result.

 

Impact on run-life

With unplanned shutdown we put the impact on run-life before the loss of production, because when unplanned shutdown happens, very often it becomes a life/death situation. For every complete shutdown, the damage to the mechanical and electrical system is hundreds of times worse than the fluctuation.

i) Mechanically, every time the system restarts, it not only tests the shaft, connections and bearings etc., and also courses tiny fractures on those items, which will lead to shortening run-life.

ii) Foreign object, when a well trips. If there is foreign object like sands presence in the tubing, it will sit above the discharge of the pump. When the system restarts, it is always a challenge blowing them off, very frequently we get repeating over-load. If the sand problem mixed with other object such as paraffin, an unplanned shutdown could mean a death sentence for the lifting system.

iii) Electrically, during those attempts of restarting the electrical system gets a lot of surges, the load is very common built up to more than 150% of its capacity which will course severe damage.

 

Production loss

When unplanned shutdown happens it is obvious that the production will suffer loss, it’s just the matter of how much loss and the answer is depending on how long the shut-down will be. If it’s protective shut-down due to the settings, if lucky we’ll just need to wait for the operator to come to do the reset, if in environment like in Iraq this could means up to a week. If not lucky that could means failing to restart and waiting for a pull out.

However, if a shutdown is beyond the control of ALPOS (i.e. power fluctuation/black out), a rapid restart is always recommended, not just for the sake of production but also to avoid the high failure rate of long time shut down, especially for those wells with problems like sand, paraffin etc. So, whenever those shutdowns happen, ALPOS will try to auto start the well remotely when the well gains power, those auto starts will be closely monitored by engineers on duty to make sure the artificial lifting system operates in its expected range. Under those circumstances, ALPOS will map out a restarting scheme considering the structures of the power grid to make sure the startups won’t trigger another failure to the Power Grid.

With above procedure, we don’t need to wait for the operators to manually start well by well. Combing with the unnecessary shutdown ALPOS help to avoid, the total downtime is expected to be minimized to a very low level (10% of current average).

 

Fluctuation in artificial lifting production usually causes production loss and brings impact to system run-life. This article describes how ALPOS deals with fluctuation in artificial lifting production.

 

Production loss

By responding to every fluctuation, ALPOS will adjust the artificial lifting system into its best configuration to adapt to the ever-changing operating environment.

For example, very often we are trying to push the well to produce at its highest capability. When a certain well is being pushed to its limit, very often the single phase or bi-phase flow would turn into much more complicate multi-phase flow which contains gas. The system performance curve would start to fluctuate in presence of gas. When ALPOS is signaled that a well is fluctuating due to gas problem, one of the real-time action would be lower the frequency to stabilize the reservoir pressure and turn the flow back to oil/water flow, and ALPOS will try to increase the frequency back by smaller steps if it senses the reservoir supply is recovering.

If no corrective method were carried out the system would be performing like this:

With ALPOS the performing curve would be like this:

The increase of production under ALPOS is estimated to be 7%~15%.

 

Impact on run-life

For every time the fluctuation strikes, the mechanical and the electrical systems suffer.

i) Mechanically, take above ESP in gassy condition for example, working under gassy condition means shorter run-life or even potential failure, depending on volume percentage of the gas. By less exposing our artificial lifting system to the gassy condition, the run-life will be extended, at least by the number of months ALPOS keep the well right above the bubble point pressure. According to the above comparison, the run-life would be extended for at least 10 months for the mechanical system.

ii) Electrically, for every time there is a load surge/drop from the mechanical system, there will be a corresponding reaction to the electrical system, which means the electrical system will experience an overload/underload. By experiencing that, the system will be aging faster than expected. As we can see from above graph, ALPOS will keep the system away from continuous fluctuation, by which it increases the run life of the electrical system.