State-of-the-Art Summary

Reducing Risks and Costs Through Technology
by Karl Lang, Hart/IRI Fuels Information Services

Two Department of Energy (DOE) initiatives managed by the National Energy Technology Laboratory (NETL) have generated more than 50 new ideas for independent producers seeking to reduce the risk of investment or reduce the costs of operating older, marginally profitable oil and gas fields. The Technology Development with Independents Program has provided funds for 46 projects since its inception, with industry contributions to these projects providing more than 70% of the total required investment. The Stripper Gas Well Program has enabled five more projects specifically focused on low productivity gas wells.

In this article we take a look at four of these projects that focus on reducing the risks of decision-making for investments designed to increase production, reducing the manpower required to manage production, and reducing the operating costs of marginal production. All of this effort is commonly directed towards the goal of keeping domestic wells profitably on stream. See PTTC's website www.pttc.org for more detailed information.

Project 1. Reducing Risk When Deciding to Re-Establish a Waterflood

The question facing the operator was a common one: Does it make economic sense to re-establish a discontinued waterflood in an older field that is approaching its economic limit? In this case, several new techniques were employed to extract the most value from the limited amount of data available, making it possible to quantify the economic outcome of various operating options. In the end, the operator chose to re-establish the waterflood and the field's subsequent performance (over 12 months) is holding reasonably well to the forecast.

The 520-acre Shugart Queen Sand Penasco (SQSP) Unit oil property in the northeast corner of Eddy County, New Mexico, was first developed in 1939 and placed on waterflood in 1974. Water injection was suspended in 1994. Production ranged between 10 and 100 barrels per month (0.3 to 3.3 BOPD) and the Unit was facing shut-in and abandonment unless additional recovery potential could be identified. The operator, MNA Enterprises, believed there was waterflood potential because the secondary to primary recovery ratio for the SQSP Unit was only 0.7 while other units located in the same trend averaged 1.2. A simulation study would be preferred, but unfortunately, as is often the case in older fields, only one 60 year-old electric log and four gamma ray-neutron count rate perforating logs were available.

The first step was to expand the study area beyond the 520-acre Unit to include all wells drilled in the four contiguous sections. This resulted in 47 well locations, of which 9 had a suite of modern logs, 10 had gamma ray and neutron count rate logs, and 17 had gamma ray logs. There was also one well with 10 feet of core through the producing zone. The challenge was to find a way to use the modern logs available on nearby wells to generate a map of reservoir properties suitable for reservoir modeling across the Unit.

The strategy for accomplishing this had five steps:

Convert the modern logs to gamma ray-neutron count rate format.
Train a neural network to "see" modern log cross plot porosity given GR and NCR logs.
Correlate porosity with permeability using core measurements.
Correlate permeability with water saturation using core measurements.
Map the three properties using geostatistical algorithms.

The maps that resulted from this strategy were used to define input parameters for the reservoir simulation model used to history match both primary and secondary production history for the Unit. The final reservoir model developed from that history match was then used to evaluate different operational scenarios.

The model results indicated that re-initiating water injection at 300 BWPD with the existing waterflood pattern would result in 7,000 incremental barrels of oil (double the estimated remaining oil recovery under then-current operations) over a five-year period. The study prompted the re-initiation of the waterflood and, as of November 2001, injection is 600 BWPM into three wells and production is about 8 to10 BOPD from five wells.

Two techniques were employed to compensate for limited data. An artificial neural network was used to correlate older logs with the modern logs and geostatistical mapping software was used to interpolate and map reservoir properties across the Unit. A neural network uses a known data set to train a computer to relate one set of data with another, and then predicts outcomes based on that learned "behavior" (see www.pttc.org for further description).

Project 2. Reducing Risk When Deciding to Initiate a Waterflood

How can relatively sparse, publicly available data be used to quickly determine the waterflood potential for selected fields? This was the problem Coral Production Company (Coral) faced when considering the installation of a waterflood in the Herboldsheimer Field in Western Nebraska. In this example, fuzzy logic was used to prioritize the relevance of available parameters on the outcome of the proposed waterflood and a trained neural network was used to develop a correlation to estimate future oil production.

Coral was considering a waterflood in the 5,300 foot J-Sand of the Cliff Farms Unit (formerly the Herboldsheimer Field "J" Sand Waterflood Unit), in the Western Nebraska Panhandle section of the Denver-Julesberg Basin (D-J Basin), Cheyenne County, Nebraska. As is often the case, the detailed information necessary to perform a reservoir simulation was unavailable and acquiring the information cost prohibitive. However, a significant amount of basic information was available in the public domain through the agency responsible for regulation of the state's oil and gas resources (e.g., field location, discovery date, producing zone, depth, water injection date, number of wells, and oil, gas, and water production records). Additionally, oil gravity, estimates of average porosity and permeability, bottomhole pressures, net pay, connate water saturation, and formation volume factor are sometimes available from waterflood unitization application files and hearing records. The challenge was to develop a correlation among these parameters that could be used to at least roughly predict waterflood performance.

Project 2—Coral Production
Results of Correlation Developed Using Neural Network

The ratio of secondary barrels produced to barrels produced during primary production (S/P ratio) was chosen as a quantitative ranking factor for secondary recovery success. The compiled parameter values were used to construct correlations between the dependent variables (those parameters which might affect waterflood success) and the S/P ratio for each waterflood unit. As might be expected, initial analysis using conventional cross-plots indicated little or no correlation between single dependant variables and S/P ratio. The assumption, however, was that some multivariant relationship among the variables might exist.

Fuzzy ranking was used to identify numerical and visual correlations between S/P and individual parameters. An artificial neural network was applied to develop a correlation between the dependent variables likely to influence waterflood recovery and S/P ratio. A two-layer, 6-3-1 neural network (see www.pttc.org for further description) was trained to develop this correlation.

The correlation was applied to predict the S/P ratio and waterflood recovery (primary recovery times the S/P ratio) for the Cliff Farms Unit (see Figure). The correlation predicted a S/P ratio of 0.315, which, based on 326,000 barrels of primary oil, translated into incremental waterflood recovery in excess of 100,000 barrels of oil. A waterflood pilot project was initiated in July 2000.

Project 3. Reducing the Cost of Identifying Candidates for Remedial Work

One problem many small operators face is the "can't-afford-to but can't-afford-not-to" dilemma. They have a number of marginal wells, some of which might profitably respond to remedial work, but the effort to gather data and evaluate each and every well to determine which ones are the best candidates can be daunting. This can be particularly true for stripper gas wells. What is needed is a cost effective method for determining which of these wells have the highest likelihood of responding to performance enhancing investments, so they can be targeted for more serious evaluation. Previous DOE research with BDM-Oklahoma resulted in a software application called Priority designed to help operators quickly locate problem wells by identifying abnormal production rate declines. But even when operators identified decline abnormalities, the underlying production problems often went undetected. The proposed solution was the development of a procedure employing data collection forms and decision trees to systematically and economically identify the source of abnormal production declines and suggest corrective action. James Engineering, Inc. of Marietta, Ohio was selected to study the causes and effects of problems associated with abnormal production declines in stripper gas wells. A study group of wells producing primarily from the Clinton Sand was located in Ohio.

Each well in a sub-group of 376 wells was reviewed for abnormal production decline by comparing a Clinton Sand production decline type curve to the well's historical production rate vs. time semi-log plot. Through a careful assessment, the circumstances and source of each well's problem(s) were determined for the entire sub-group. The results showed that more than 46% of the abnormal production declines were caused by fluid accumulation, 24% by gas gathering restrictions, 23% by mechanical failures, 4% by reservoir depletion, 2% by metering inaccuracies, and less than 1% by reservoir damage. In only 2% of the cases was the cause of the decline undetermined.

Based on the information generated by the analysis, three Data Collection Forms were developed to assist in analyzing problem wells. These forms provided a rigorous, disciplined framework through which information could be consistently gathered and production characteristics that would typically result in abnormal behavior or changes in flowing bottomhole pressure identified. In addition to the three data forms, a Decision Tree Triage Form was developed. Following the decision tree insures that the most common problems are diagnosed and solved by the simplest analysis, only expanding the analysis as the problem requires.

Twenty-four Clinton Sand wells with abnormal production declines were identified based upon decline curve analysis and the Priority monthly production monitoring reports. Data Collection Forms were developed for each and the Decision Tree Triage was followed for each well. Each well was reviewed to determine the estimated cost to remediate and resulting economic indicators. Two of these wells, both having the most common problem of fluid accumulation, were selected for a field demonstration of the methodology. Remedial work carried out for both wells will pay out in between 4 and 11 months. The methodology was shown to be effective as a way to categorize and prioritize well problems. The decision trees, data collection sheets, and economic analyses were refined and incorporated into a procedural guide available from NETL.

Project 4. Reducing Operating Costs Through a Low-Cost Well Surveillance System

Another example of cost reduction involves the design of a low-cost method for monitoring lift conditions in marginal oil wells. Although desirable, the sheer magnitude of the number of wells precludes most operators from manually monitoring marginal wells. Opportunities to correct well problems promptly and maximize production efficiency are lost.

Working with DOE, the Hunter Living Trust (now Vaquero Energy Inc.), a Santa Barbara producer, estimated that successful development and operation of such a system in the 225-well Edison Field of Kern County could result in improvements of up to 1,000 BOPM over the 30,000 BOPM average. Electricity costs would be reduced as worn pumps could be identified and repaired promptly, which would increase overall average pump efficiency. The estimated cost savings from using such a system were $4,000 per month in personnel time and $2,000 per month in electricity costs, due to greater pump efficiency. The result has been the design and testing of a Marginal Expense Oil Well Wireless Surveillance (MEOWWS) system.

Even low production rate pumping wells generate a variety of mechanical signals. Some of these signals, when properly monitored, provide data that can be used to determine when a well's performance begins to deviate from optimum operating conditions. Existing, commercially available systems do this well, but at a cost of thousands of dollars per well and the need for expensive electronic data transmission systems. The challenge was to design, evaluate, install and test inexpensive, commercially available, "off the shelf" wireless surveillance devices and sensor technology that might be adaptable to measure and transmit signals.

Discussions regarding non-oil field technology development and computational micro devices were conducted with Dr. Massoud Mehdizadeh, a petroleum engineering consultant and faculty member at California Polytechnical State University. A vendor was located that offers wireless vibration surveillance devices designed to track performance trends of rotating machinery.

Sensor units were installed on four wells on the polish rod and walking beam near the horse-head end of the pumps. Since the sensors are self-contained, no special wiring or complicated installation was required. A nearby radio signal repeater, a base radio signal receiver, and a base station PC (which interfaces with the base radio signal receiver to store and process the vibration data) were acquired and installed. Signals related to the vibration behavior of the pumping unit were transmitted to the base station computer, which activates an alarm, alerting operating personnel when a well deviates from its normal, pumped-off condition.

Data indicated that the acceleration sensors were able to detect fluid pound when the well pumped-off. Results indicated that wells that are not pumped-off have vibration behavior that fits a normal distribution, while pumped-off wells have variations in vibration acceleration that consistently exceed 2 and 3 standard deviations. Future work is being considered to test additional sensors, include more wells in the project and determine (1) the best sensor location (beam, polish rod, etc.), (2) the best sensor orientation (vertical or horizontal), (3) minimum (400 Hertz or lower) sensor frequency, and (4) the minimum sample rate (to extend battery life). Feasibility was demonstrated, but more work is required to improve and develop a robust system for commercial applications.

Identifying Ways to Reduce Risks and Costs

These four projects are some examples of new and innovative ways independent producers are working to maintain production from marginal wells. DOE plans future solicitations within its Technology Development with Independents Program and the Stripper Well Consortium to further assist operators in testing new technologies.

For a complete version of this article, including references, discussion of one additional software product, and a sidebar on neural networks and fuzzy logic techniques, visit www.pttc.org/.

Table of Contents	Vol. 7, No. 4 4th Quarter 2001