Optimization model of material balance for reservoir energy survey analysis and control

Introduction. When monitoring the development of hydrocarbon fields, the energy state of the reservoirs is monitored. An inaccurate understanding of the current distribution of reservoir pressure leads to the wrong strategy for the development of residual oil reserves. Isobar maps are constructed to assess reservoir pressure for productive formation. The simplest and most common method of map generation (by interpolating the values of the measurements) has a high sensitivity to the coverage of the well stock by well tests and does not take into account the dynamics and compensation of fluid withdrawals. To eliminate these shortcomings, it is proposed to use the method of multi-tank material balance. Objectives. The purpose of the work is to show the possibility of using the multi-tank material balance method (MMB) to reduce the risks related to low current reservoir pressure at the stage of well workover planning under conditions of limitations on the number and duration of well tests and taking into account the history of production / injection, as well as to predict the dynamics of reservoir pressure in the drainage area. Methods. In the MMB model, for each well, its own block (tank) is specified, for which the mathematical balance equation is drawn up, taking into account the crossflow between the blocks. The transmissibility values are obtained iteratively by numerically solving the problem of minimizing the loss function of the discrepancy between the calculated reservoir and bottom hole pressures and their actual values. Results. The paper provides examples of good convergence of the calculated pressures by the MMB method to the actual build-up test results at the wells of the Ural-Volga fields. The possibility of predictive calculations to optimize the reservoir pressure maintenance system is shown. The practical advantages of the method are: relative simplicity of model, automated adjustment of interblock transmissibility. Discussion. The field of application of the MMB method is determined by the tasks in which it is necessary to determine the reservoir pressure in conditions of a lack of fresh measurements. The MMB methodology is applicable to assess reservoir pressure in order to remove risks before well workovers, as well as to predict the optimization of the reservoir pressure maintenance system.

A Case Study on Field F Multiphase Flow Meter: How is it Better than a Conventional Test Separator?

Multiphase flow meters (MPFM) have been known save costs for new installations, are compact and as effective as a test separator. Field "F" is a green field with 2 wells and has been producing since 2018 from the same reservoir. The test facilities consist of an MPFM, and F flows to a hub called Field "G". Towards Q2 of 2019, there was a significant increase in production rates from both wells without any changes to surface choke size and without enhancement jobs performed. Added to that, reservoir pressure showed steady depletion. Daily production allocation for F showed lower than usual reconciliation factor when combined with G hub production. This suboptimal allocation raised doubts about the MPFM well test readings which launched a full investigation into the accuracy of the meter. From the offshore remote monitoring system, the first suspect was the increased inlet pressure causing parameters to be out of the MPFM operating envelope range. However, after further checking, there were other pressing issues such as faulty transmitter, and low range sensors. As these issues were being dealt with amidst the COVID-19 pandemic, the process to fix the meter was longer than usual. Rectification involved troubleshooting the MPFM post performing Multi Rate Tests, back allocation check to hub production and PROSPER/GAP model matching to check on the credibility of the well tests. These efforts were made due to budget cuts, as there was no advantage to bring onboard an entire well test package (separator) to test the F wells. Post several rectifications, the liquid, gas and oil rates were within 10% difference from allocation meter back allocation and PROSPER model calculation. Reconciliation factor for field G has also increased to normal range of 0.92 to 0.95. However, the rectification also showed a significant drop in metered rates, proving that the MPFM was indeed generating incorrect well tests since Q2 2019. The drop was higher than 30% in gross production rates which lead to a better understanding of the reservoir, and corrections to be made to dynamic models for any future development projects. This hence proves that even with the similar reservoir properties in both wells, the MPFM well tests still require vigorous checking and should not be treated in the same way as a test separator. This paper will describe the efforts by surface and subsurface faculties to ensure the quality of well tests from the MPFM. For future projects considering the MPFM installation, best to frequently quality check the MPFM well test figures with a test separator. However, if that option is not feasible, the efforts in this paper can act as a guide for the field.

A Novel Well Test Data Analyzer and Process Optimizer Using Artificial Intelligence and Machine Learning Techniques

Well testing is one of the vital process as part of reservoir performance monitoring. As field matures with increase in number of well stock, testing becomes tedious job in terms of resources (MPFM and test separators) and this affect the production quota delivery. In addition, the test data validation and approval follow a business process that needs up to 10 days before to accept or reject the well tests. The volume of well tests conducted were almost 10,000 and out of them around 10 To 15 % of tests were rejected statistically per year. The objective of the paper is to develop a methodology to reduce well test rejections and timely raising the flag for operator intervention to recommence the well test. This case study was applied in a mature field, which is producing for 40 years that has good volume of historical well test data is available. This paper discusses the development of a data driven Well test data analyzer and Optimizer supported by artificial intelligence (AI) for wells being tested using MPFM in two staged approach. The motivating idea is to ingest historical, real-time data, well model performance curve and prescribe the quality of the well test data to provide flag to operator on real time. The ML prediction results helps testing operations and can reduce the test acceptance turnaround timing drastically from 10 days to hours. In Second layer, an unsupervised model with historical data is helping to identify the parameters that affecting for rejection of the well test example duration of testing, choke size, GOR etc. The outcome from the modeling will be incorporated in updating the well test procedure and testing Philosophy. This approach is being under evaluation stage in one of the asset in ADNOC Onshore. The results are expected to be reducing the well test rejection by at least 5 % that further optimize the resources required and improve the back allocation process. Furthermore, real time flagging of the test Quality will help in reduction of validation cycle from 10 days hours to improve the well testing cycle process. This methodology improves integrated reservoir management compliance of well testing requirements in asset where resources are limited. This methodology is envisioned to be integrated with full field digital oil field Implementation. This is a novel approach to apply machine learning and artificial intelligence application to well testing. It maximizes the utilization of real-time data for creating advisory system that improve test data quality monitoring and timely decision-making to reduce the well test rejection.

Case History of Matrix Acid Stimulation using Specialized Acid Composition for High Temperature Carbonate Formation and Long Interval Section

Alur Siwah field is located onshore in Block A PSC of PT Medco E&P Malaka, Aceh Province. Six of the ten drilled wells proved significant gas column in Peutu limestone and Tampur dolostone. Well tests indicated gas rates in the range of 0.2 – 42 MMSCFD from selected intervals in both formations. Estimated permeability values from well tests are in range of 0.6 – 3.7 mD. During drilling campaign in 2018 three wells was drilled with total depth around 9,500 ft TVD. AS-9A, AS-11 and AS-12 wells penetrated Peutu Limestone and TD was 213 ft TVD above common GWC. These three wells were completed with open hole and pre-drilled liner, the interval length ranging from 300 to 500 ft-MD. Since Peutu limestone has low permeability the reservoir needs stimulation to increase productivity, maintain gas sales according to GSA (Gas Sales Agreement), and optimize reservoir depletion. Matrix acidizing treatment was applied to remove formation damage. The method was proven successful in previous well at Alur Siwah field in 1990’s. Peutu limestone challenges are high temperature (360 degF), high CO2 (up to 25%), high H2S content (up to 12,000 ppm), long interval open hole section (300-500 ft-MD), and water encroachment risk from water bearing zone. High temperature will accelerate acid reaction, and premature reaction might occur before reaching the reservoir. High CO2 & H2S might cause corrosion at completion string. Penetration into water bearing could cause water encroachment and water loading issue. With proper fluid selection, acid placement method, volume treatment design and execution, matrix acidizing can be applied safely and successfully to stimulate high temperature gas wells which have long interval open hole section completed with pre-drilled liner without water loading issue. This paper covers the application of acid stimulation in Alur Siwah field, well completion, post treatment well performance, best practices and lessons learned.

A new method for monitoring wells productivity index dynamics

А method for monitoring wells productivity index dynamics from the moment of its start-ups is proposed. It allows detecting candidates for conducting well tests (WT) and well interventions for the increase of a daily production. The method is based on an integral analysis using pressure maps and well tests. Application of this method on the Salym group of oilfields has allowed concluding about wells stock status, about productivity index dynamics in time, also to make an assumption about the reasons for its change. The analysis showed that productivity index relative changes in horizontal wells lower than in slanted wells.

Extensive Well-Testing Operations Provide Insight Into Khuff Reservoirs

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 203441, “Lessons Learned From Extensive Well-Testing Operations in Khuff Formations Offshore Abu Dhabi,” by Florian Hollaender, SPE, Schlumberger, and Mahmoud Basioni and Ahmed Yahya Al Blooshi, ADNOC, et al., prepared for the 2020 Abu Dhabi International Petroleum Exhibition and Conference, Abu Dhabi, held virtually from 9-12 November. The paper has not been peer reviewed. An extensive appraisal campaign was performed in the Khuff reservoirs offshore Abu Dhabi, with multiple appraisal wells drilled in different fields. Those wells were evaluated using detailed logging campaigns and then subjected to well tests, usually through drillstem testing for targeted intervals. The interpretation of well tests, combined with advanced petrophysical analysis, formation-test data, and production logs, provided insight into the nature of the Khuff reservoirs. A wide range of responses was observed, from tight to highly productive, but not necessarily with clear previous indications of deliverability or inflow intervals. Overview of the Khuff Formations The key characteristics of the Khuff formations offshore Abu Dhabi have been well-documented in previous work and can be summarized by the following: Low porosity and permeability carbonate reservoirs, where natural fractures are critical contributors to flow Properties vary widely laterally, with significant uncertainty regarding connectivity Variations in stress and petrophysical properties can be significant and affected by diagenetic and tectonic history These reservoirs present significant challenges for development planning. Previous studies have shown that it can be difficult to relate production performance to standard petrophysical analysis directly and that the presence of fractures - in particular, critically stressed fractures - in the vicinity of the wellbore is an essential factor for production performance. Productivity also was found to vary by several orders of magnitude within the same reservoir depending on the field and lateral location of a given well. The presence of natural fractures has been recognized as a major contributor to flow in tight gas reservoirs; however, this raises several questions related to assessing formation potential. First, the nature of the fractures must be evaluated. Some will contribute to production, while others will remain sealed. Equally importantly, identifying zones with promising porosity developments is not a solid indicator of production expectations. Well-Test Observations With more than 20 drillstem tests performed in the Khuff reservoirs during a 4-year period, the first observation is the wide range of reservoir responses encountered, with an apparent lack of consistency within a given reservoir or field.